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Research Papers Lan Based Examination System In Ancient

Background

Without healthcare professionals, there are no health services. There is a direct correlation between availability of health workers , payment of health services, and population health outcomes (Anand 2004; Hongoro 2004). Currently, there is a shortage of healthcare professionals worldwide, especially in low- and middle-income countries (LMIC; AAMC 2015; WHO 2013). The poorest communities of Africa, Asia, and Latin America have less than 10% of the world's trained healthcare workers, while they face 80% of the global burden of disease and death. The shortage of healthcare workers, coupled with the added burden of ‘brain drain’, defined as the emigration of highly trained or qualified people from a particular country, is aggravated by the inadequacy of many training programmes (Chen 2010). Addressing these shortfalls through adequate training of the healthcare workforce, especially post-registration medical doctors (physicians), requires innovative cost- and time-effective methods.

ELearning (i.e. the use of technology and electronic media to disseminate information for the purpose of education) may be one such innovation. ELearning encompasses a variety of interventions, characterised by the tools, contents, learning objectives, pedagogical approaches, and setting of delivery. ELearning can include, but is not limited to, online and offline computer-based eLearning, Massive Open Online Courses (MOOCs; Yuan 2013), virtual reality environments (Westbrook 2014), simulation (Alinier 2014), mLearning (Trelease 2015), and digital game-based learning (DGBL; Sadera 2014).

This review is part of a series of Cochrane reviews assessing the scope and potential impact of a range of different types of eLearning, for different levels of healthcare professional education and training, and will focus on the use of internet- and local area network-based interventions for physicians' education. Local area networks (LAN) refer to intranet networks that connect computers and other devices within institutions or organisations.

Description of the condition

Medical education has undergone rapid changes in the last decade, primarily due to the expansion of the internet, advances in diagnosis and management of diseases, and healthcare delivery (Bleakley 2011; Chen 2007; Clark 2002, Cooke 2006). The traditional model of medical education has evolved into a dynamic system, moving from an instructor to student focused presentation session, to a student-centred process where students are able to learn at their own pace. Furthermore, the student’s role has changed from being a receiver of knowledge and content to being a learner, and the instructor’s role has evolved to that of a mentor, guiding students to acquire knowledge and improve their learning skills (Méndez-Vilas A).

Lifelong learning is a concept taken up by governments and educational institutions, worldwide, to acknowledge the need for continuous learning, regardless of the profession. Lifelong learning has always been formally considered an ethical obligation of doctors (Siddiqui 2003), and is critical in order to keep up with medical advances and ensure high quality healthcare (Engelbrecht 2004).

Traditional “lecture and test” methods of teaching provide the learners with plenty of information, but do not give them the skills to update and replace this knowledge as needed (Shaughnessy 1999). The content, structure, and mode of delivery of these training programs often fail to equip healthcare professionals with the necessary skills and knowledge needed to keep pace with the changing health needs of the population they serve (Frenk 2010).

ELearning has the advantage of flexibility of access and time. Research shows that learning is influenced more by the content and instructional strategy, than by the type of technology used to deliver the content (Ally 2004); the design of the course determines its effectiveness on learning (Rovai 2002). There are many learning theories. According to behaviourists, it is the observable behaviour that indicates whether or not the learner has learned something, and not what is going on in the learner’s head (Anderson 2008). In response, other educators state that not all learning is observable and that there is more to learning than a change in behaviour (Anderson 2008). As a result, there was a shift away from behaviourist to cognitive learning theories (Anderson 2008). According to cognitive psychologists, learning involves the use of memory, motivation, and thinking, and reflection plays an important part in learning. Learning is an internal process; the amount learned depends on the processing capacity of the learner, the amount of effort expended during the learning process, the depth of the processing, and the learner’s existing knowledge structure (Ally 2004; Anderson 2008). In the early 1990s, educational theory moved to constructivism. Constructivist theorists claim that learners interpret information and the world according to their personal reality, and that they learn by observation, processing, and interpretation, and then internalise the information into personal knowledge (Cooper 1993; Wilson 1997). Learners learn best when they can contextualise what they learn for immediate application and personal meaning (Ally 2004; Anderson 2008). In 2004, theorist George Siemens advanced a learning theory for the digital age; he called it connectivism. The theory addressed the role technology plays in the learning process. According to connectivism, learning happens in many different ways. Learning is a process of connecting specialized nodes or information sources, such as, courses, email, communities, conversations, web searches, email lists, blogs, etc. Courses are no longer the primary conduit for learning. A learner can exponentially improve their own learning by plugging into an existing network. Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning (Siemens 2014).

When the schools of thought from behaviourism, cognitivism, and constructivism are analysed closely, many overlaps in the ideas and principles become apparent. Behaviourism focuses on how learning behaviour is shaped through positive or negative reinforcement; constructivism focuses on communication between learner and teacher; cognitivism focuses on comprehension, abstraction, analysis, synthesis, generalisation, evaluation, decision-making, and creative thinking; and lastly, connectivism focuses on learning through specialized information sources and keeping up-to-date. Therefore, the design of online learning materials should incorporate principles from all four theories.

Description of the intervention

Information and communication technology (ICT) has transformed the way information is exchanged and shared around the world. Information and communication technology-based learning interventions allow doctors to learn anywhere, at any time, and provide opportunities for interactive communication and networking.

In addition to its increasing use in undergraduate medical and health professional education (George 2014), online eLearning is gaining popularity in post-registration (i.e. continuing) medical education, which is evident from the increasing number of studies over the years. There is no sharp division between continuing medical education (CME) and continuing professional development (CPD), as during the past decade, CME has come to include managerial, social, and personal skills; topics beyond the traditional clinical medical subjects (Peck 2000).

eLearning can be defined as 'an approach to teaching and learning, representing all or part of the educational model applied, that is based on the use of electronic media and devices as tools to improve access to training, communication, and interaction, and that facilitates the adoption of new ways of understanding and learning' (Sangrà 2012). eLearning differs from traditional learning in the approach and medium by which it is delivered (Masters 2008).eLearning can use a full electronic approach, which is entirely driven by technology, or use a mix of traditional and computer–based methodologies (i.e. blended learning). Blended learning might be more suitable for healthcare training because of the need to combine hands–on skill–based training at a practical level and knowledge-based self–directed learning (Duque 2013; Makhdoom 2013; Rowe 2012).

Internet- and LAN-based eLearning represents a further evolution of computer–assisted or computer-based eLearning and is an important tool with the potential to transform post-registration medical education (George 2014; Potomkova 2006). In recent years, nearly all medical schools in the USA and Canada used some form of online course materials as part of their CME for physicians (Cook 2010). Online eLearning approaches vary widely in configuration (e.g. tutorial, asynchronous discussion, live conferencing, etc.), instructional methods (e.g. practice exercises, cognitive interactivity), and presentation (Cook 2010).

For the purpose of this review, we define internet- or LAN-based eLearning interventions as those that require the use of a 'Transmission Control Protocol' (TCP) and an 'Internet Protocol' (IP) as a standard for the learning activities. These may also be referred to as being ‘online’, ‘web-based’, or ‘networked’. We will include studies that contain interventions where a TCP/IP connection is essential in order to fully participate. In the absence of a network connection, there would be a loss of both functionality and usability to such an extent that the originally intended purpose would no longer be provided and the user interaction would end.

How the intervention might work

In contrast to didactic lectures, learners using online eLearning can access a network-based tutorial at any time of the day. They are provided with constant updates of content, individualised learning (Clark 2002; Cook 2005), novel instructional methods (Ericsson 2004), and automated assessment and documentation (Cook 2005a). These interventions present numerous opportunities for universities that include: reduced cost associated with the delivery of educational content (Lincoln 2011), improved scale of educational developments (Clarke 2001), and the ability to provide educational content, relevant experts and novel curricula, to learners in regions that have traditionally been difficult to access (Miller 2012).

ELearning, especially internet-based delivery, can be interactive, and allows immediate feedback to facilitate learning, and improve cognitive skills and study habits. In addition, internet- and LAN-based eLearning allows the transfer of a greater amount of multimedia than non-networked methods, partially due to the increased availability of wireless connections and enabled linked devices. It may be particularly effective in eLearning, due to the vast amount of personal and group interaction, wider potential access, and reduced physical materials required (e.g. CD-ROMs;George 2014; Rasmussen 2014).

There are a few limitations for online eLearning; first, access to appropriate technology can be a problem for learners in developing countries. Second, learners might find it difficult to access graphics, images, and video clips because of poor equipment and network connectivity. Third, the necessary infrastructure may not be available and affordable to all. Lack of adequate peer support and interaction between learner and tutor could also be a problem (Bouhnik 2006). If online eLearning developers could address these issues, they would play a major role in providing updated information to medical doctors.

Why it is important to do this review

There is an exponential increase in the adoption of eLearning for training healthcare professionals, and the number of studies evaluating its effectiveness, especially for physicians (George 2014). Evidence from randomised controlled trials (Allison 2005; Bello 2005; Braido 2012; Chan 1998; Chenkin 2008; George 2014; Houwink 2015; Hugenholtz 2008), show online eLearning to be better than traditional learning (Allison 2005; Braido 2012; Houwink 2015), or as effective (Chan 1998; Chenkin 2008; Hugenholtz 2008). Outcomes reported in these studies include learners' knowledge, skills, behaviour change, and satisfaction.

Past reviews of online eLearning focused on the impact and effectiveness of internet-based learning in health professionals’ education (Cook 2010; George 2014; Rowe 2012). There was significant methodological, educational, and clinical heterogeneity amongst the studies.These reviews highlighted the need for a focused review on online eLearning for physicians, since there is still uncertainty about what types of online eLearning are helpful, (i.e. their pedagogy, how they change clinical practices or behaviours, their duration (short-term rather than long-term), their specialty and educational context). With a growing body of literature in the last five years on specific applications of online eLearning (e.g. CME), it is critical to evaluate the evidence to understand its effectiveness, and identify training areas amenable for eLearning in physicians' ongoing education.

Our review will address the existing evidence gaps by:

  • Updating the rapidly growing body of evidence on the topic of internet- and LAN-based eLearning for physicians' education;

  • Focusing on internet- and LAN-based eLearning interventions for ongoing physician training ;

  • Evaluating the impact of such intervention on knowledge, skills, attitudes and behaviours of physicians;

  • Including evidence from developed and developing countries.

This review will provide the evidence-base needed to guide and inform future projects and policies on online eLearning for ongoing physicians' training.

Objectives

This review will evaluate the effectiveness of internet- and LAN-based eLearning for ongoing training of medical doctors, specifically looking at the impact of the learning on the learners’ knowledge, skills, attitude, and satisfaction. This review will also assess any change in clinical practices or behaviours in response to these interventions, and the economic impact (cost and cost-effectiveness) of internet- and LAN-based educational interventions.

Methods

Criteria for considering studies for this review

Types of studies

We will synthesise evidence from randomised controlled trials (RCTs) and cluster RCTs (cRCTs) of online eLearning for medical doctors, in which outcomes are expressed in quantitative terms.

We will exclude cross-over trials, due to the high likelihood of a carryover effect. Similarly, we will exclude studies in which outcomes are presented using qualitative or semi-structured quantitative interview methods (mixed-methods).

Types of participants

We will include studies in which participants are medical doctors enrolled in a post-registration medical educational programme, defined as any type of study offered after professional qualification, which is recognised by the relevant governmental or professional body that grants entry into or continued membership in the healthcare workforce, in a more independent or senior role. We will also include CME and CPD-based programs that involve the use of online eLearning interventions (such as webinars, online lectures, and online journals).

We define CME as ‘all educational activities which serve to maintain, develop, or increase the knowledge, skills, and professional performance and relationships that a physician uses to provide services for patients, the public, or the profession’ (AAMC 2015) and CPD as ‘a range of learning activities through which medical professionals maintain and develop throughout their career to ensure that they retain their capacity to practice safely, effectively and legally within their evolving scope of practice.’(HCPC 2015).

We will not exclude participants on the basis of age, gender or other socio-demographic variables. However, we will exclude studies with mixed participant groups, such as, doctors, nurses, pharmacists, pre- and post-registration healthcare professionals, in which results are not presented by professional group.

Types of interventions

We will include studies in which online eLearning interventions are used to deliver the learning content of the course in physicians' education, either as the sole or partial means (i.e. blended learning) of delivery for the purpose of teaching, learning, training, or a combination. The following are the inclusion criteria in detail.

We will include studies on:

  • Online eLearning interventions (computer-based, computer-assisted) where the learning content is delivered using the internet or LAN (Cook 2008), with or without a learning management system (LMS).

  • Interventions such as web-based delivery of tutorials (online equivalent of classroom-based lectures), discussion boards, email, and internet-mediated videoconferencing (Cook 2008).

  • Educational interventions that require internet connectivity throughout the entire duration of the intervention and CMEs that require intermitted internet connections for online discussion and evaluation.

  • Educational interventions targeted at 'physician activation'.

We will include studies that make the following intervention comparisons:

  • Internet- and LAN-based intervention versus traditional learning;

  • Internet- and LAN-based intervention versus other types of eLearning interventions;

  • Internet- and LAN-based intervention versus another internet- and LAN-based intervention;

  • Internet- and LAN-based intervention (where internet and LAN are used as the sole mode of intervention) versus blended intervention (where internet and LAN are used together with other forms of intervention);

  • Blended intervention where internet- and LAN-based eLearning is used together with other forms of intervention in comparison to traditional learning.

We will exclude the following studies:

  • Studies of educational interventions targeted at 'patient activation' alone;

  • Studies of interventions that only require an internet connection for downloading of software, slides or other educational content;

  • Studies in which an online eLearning intervention is accessed using mobile phones or tablets, as these interventions will be covered in a separate review (Tudor 2015).

  • Studies investigating computer-based educational interventions (e.g., CD ROMs), unless these interventions use the internet to disseminate information (Cook 2008).

  • Studies investigating tele-medicine, tele-health based learning interventions and video conferencing delivered through an analogue or digital telephone network or using satellite connectivity.

Types of outcome measures

This review will investigate different kinds of online eLearning content types and outcomes such as, patient-related outcomes, knowledge comprehension, intellectual skills and their applications, analysis, synthesis, evaluation and attitudes (cognitive and affective domains; Lim 2007). We will exclude studies of online eLearning that assess motor skills and skills-based learning (psychomotor domain), as they will be analysed in a separate review in the series. However, we will include studies that assess intellectually-based skills, which include reading instructions, solving problems, and other tasks that involve the recall and processing of information (Clark 1999). We will include studies that report at least one of the following primary or secondary outcomes:

Primary outcomes

We will assess the impact of internet- and LAN-based eLearning interventions on the following primary outcomes:

  • Learners’ knowledge, measured with any instrument to measure difference in pre- and post-test scores, or post-test scores only, if no pre-test has been reported. If several post-test results are available, we will use the difference between the pre-test and the first post-test. We will investigate other tests using sensitivity analyses (see section below).

  • Learners’ intellectual skills (cognitive and affective domains, e.g. critical, analytical, synthesising and problem-solving skills) measured with any instrument (e.g. pre- and post-test scores, time to perform a procedure, number of errors made whilst performing a procedure).

  • Learners’ attitudes towards patients (e.g. awareness of moral and ethical responsibilities involved in patient contact) or towards new clinical knowledge or skills, measured using any instrument.

  • Learners’ satisfaction with the overall eLearning experience, measured using any instrument, including but not limited to, satisfaction with: 1) the way the course content was presented; 2) learning objectives met by the course; 3) interaction and feedback; 4) learning delivery mode.

Secondary outcomes

The following secondary outcomes will be considered:

  • Changes in clinical practices or behaviours (e.g. reduced antibiotic prescribing, improved diagnosis, improved quality of care).

  • Cost and cost-effectiveness of the intervention.

  • Adverse or unintended effects of internet- and LAN-based intervention (e.g., patient mortality, patient morbidity, medical errors,)

We will take into account outcomes measured at all time points.

Search methods for identification of studies

We propose a search strategy in accordance with the Cochrane Handbook of Systematic Reviews of Interventions (Higgins 2011). We will define and use a common search strategy for all of our Cochrane reviews in a series on eLearning for health professional education as mentioned above in the Types of interventions section. See Appendix 1.

We will also contact the Tobacco Addiction Review Group for further assistance in developing a comprehensive and systematic search strategy of relevant databases to identify relevant literature.

We will include studies in all languages and stages of publication.

Electronic searches

We will search the following databases, adapting the MEDLINE strategy and keywords presented in Appendix 1:

  • The Cochrane Central Register of Controlled Trials (CENTRAL; The Cochrane Library, current issue)

  • MEDLINE (Ovid)

  • EMBASE (Elsevier)

  • PsycINFO (Ovid)

  • Educational Resource Information Centre (ERIC; Ovid)

  • Cumulative Index to Nursing and Allied Health Literature (CINAHL; Ebsco)

  • Web of Science Core Collection (Thomson Reuters).

We will search databases from 1990 to present. We are selecting 1990 as the starting year for our search because prior to this,use of the computer and internet was limited to very basic tasks.

Searching other resources

We will search reference lists of all included studies and relevant systematic reviews that we identify while running our electronic searches, and contact experts in the field to determine if there are further studies.

Data collection and analysis

Selection of studies

We will import all the references identified by our searches into reference management softwareand remove duplicates. We plan to calibrate the screening of studies between the review authors using the first 500 citations. Two review authors (PPG and ET) will independently screen titles and abstracts to identify studies that potentially meet inclusion criteria. We will put these references into defined groups e.g. offline eLearning, online eLearning, virtual patients, massive open online courses (MOOCs), mLearning, psychomotor skills training, and VRE. We will retrieve the full text of articles that appear to meet the criteria or for which we are unsure. Two review authors will independently assess the full text of the retrieved articles for compliance with our inclusion criteria. We will resolve any disagreements through discussion between the two review authors. If no agreement can be reached, we will consult the third review author. We will list studies which appeared to be relevant but are excluded at this stage in the ‘Characteristics of excluded studies’ table, where the reason for exclusion will be noted. Two review authors will verify the final list of included studies.

We will present the process for selecting studies in a PRISMA flow chart.

Data extraction and management

Two review authors will independently extract and manage the data for each of the included studies, using a structured data recording form. We will pilot the data extraction form and amend it according to the received feedback. In addition to the usual information on the study design and participant demographics, we will extract data on the participants, intervention, control, and outcomes measured, as well as the mode of eLearning intervention. We plan to contact study authors in the case of any unclear or missing information. We will resolve disagreements between review authors by discussion. A third review author will act as an arbiter in cases where disagreements cannot be resolved.

Assessment of risk of bias in included studies

Two review authors will independently assess the risk of bias for included RCTs and cRCTs, using the Cochrane ‘Risk of bias’ tool (Higgins 2011). We will pilot the 'Risk of bias' assessment between the review authors and contact study authors in case of any unclear or missing information. We will asses the risk of bias in RCTs for the following domains: random sequence generation; allocation concealment; blinding (participants, personnel); blinding (outcome assessment); completeness of outcome data, selective outcome reporting; and other sources of bias. For cluster RCTs, we will assess the risk of additional biases: recruitment bias; baseline imbalance; loss of clusters; incorrect analysis; and comparability with individually randomised trials. We will classify judgements of risk of bias for each study as 'yes', 'no', or 'unclear', indicating high, low, or unclear risk of bias, respectively. We will report 'Risk of bias' tables, graph, and summary in the review.

Measures of treatment effect

We will use mean differences (MD) and risk ratios (RR) to summarise continuous and dichotomous (categorical) outcomes, respectively; their variances will be expressed as 95% confidence intervals (CI). Variances will be inflated for cRCTs, taking into account the cluster size, number of clusters, and the intra-class correlation coefficient (ICC). If studies measure the same outcome using different scales, we will estimate the standardized mean difference (SMD) by dividing the study mean difference between groups by the standard deviation of outcomes among participants.

Unit of analysis issues

For cluster RCTs, we will attempt to obtain data at the student or learner level. In cases where the statistical analysis of the cluster RCT has already been adjusted for the clustering of data, we will extract the reported effect estimates and use them directly in our analyses. In cases where individual data are not available in the study report, we will contact the trial author(s) to request the data. We will meta-analyse all available data using the generic inverse variance method in Review Manager 5 (RevMan 2014), which accounts for the clustering of data. When access to student-level data is not possible, a summary effect measurement will be extracted for each cluster. We will use the number of clusters as the sample size, and analyse as if the trial was individually randomised. However, it must be noted that this technique will reduce the statistical power of the analysis.

Dealing with missing data

We will contact the original investigators to request missing data. If we are unable to obtain them, we will use data available from the available study reports and assess the risk of bias through the criterion ’incomplete outcome data’. We will not impute missing data, and will discuss all assumptions and subsequent procedures used to deal with missing values in the review. Where possible, we will conduct analyses on an intention-to-treat basis.

Assessment of heterogeneity

We will decide if it is appropriate to pool our measures of effect by assessing if the included studies are similar enough (in terms of their population, intervention characteristics, and reported outcomes) to draw meaningful conclusions. If a meta-analysis of the included studies is indicated, we will assess statistical heterogeneity by visual inspection of the effect estimates in the forest plot, and by calculating the I² statistic (Higgins 2011). In the case of a high degree of heterogeneity (I² greater than 0.5), we will explore possible reasons for variability by conducting subgroup analyses. If we detect substantial unexplained clinical, methodological, or statistical heterogeneity among the included studies, we will use a narrative approach to data synthesis.

Assessment of reporting biases

We will assess reporting bias qualitatively, based on the characteristics of the included studies (e.g. if only small positive studies are identified for inclusion), and if information that we obtain from contacting experts and authors suggests that there are relevant unpublished studies. If we include at least 10 studies, we will assess reporting bias using a funnel plot regression, weighted by the inverse of the pooled variance. We will interpret a regression slope of zero as absence of small study bias.

Data synthesis

Data will be reported using Review Manager (RevMan 2014). We will enter extracted data into tables, grouped by study design and type of intervention, to create a descriptive synthesis.

Using Miller's classification of clinical competence (Miller 1990), the different types of tests for learners' knowledge and skills will be grouped and analysed together. For example, Multiple Choice Questions (MCQs) assessing knowledge (i.e. knows) will be analysed together, and essay questions assessing competence (i.e. knows how) will be analysed together. The focus will be on the testing method rather than the delivery method (i.e. if skills are assessed by a knowledge test, it will be categorised as knowledge).

For learner’s attitudes, we will group and analyse the different types of assessment as cognitive, behavioural, or affective attitudes, as described in Martin 2002. Learners’ satisfaction will include the satisfaction and attitudes towards the learning intervention to which they were exposed. We will assess learners’ attitudes and satisfaction as a narrative, as the preliminary work conducted by the Global eHealth Unit suggests that there is a high level of heterogeneity in the operational definition of these outcomes across different studies (George 2014; WHO 2013).

Where studies report more than one measure for each outcome, we will use the primary measure as defined by the primary study authors in the analysis. Where no primary measure has been reported, we will calculate a mean value of all the measures for the outcome and use it in the analysis. If meta-analysis is feasible, we will use a random-effects model, which provides a more conservative estimate of effect and can be used where there is moderate heterogeneity. We will include an intention-to-treat analysis of the results in the meta-analysis.

Subgroup analysis and investigation of heterogeneity

We anticipate that the following subgroup analyses are likely to be appropriate, comparing:

  • low-, middle-, and high-income countries;

  • type of internet and LAN intervention used;

  • post-registration medical professional education according to sub-specialities, using International Standard Classification of Occupations (ISCO-08);

  • quartiles of adherence or time spent on the intervention. We will recalculate and present the measure of adherence or time spent on the intervention as a percentage to account for the difference in intervention duration between studies.

We acknowledge that there are other subgroup analyses that could be performed; e.g. comparing interventions according to their pedagogical aspects, and interactivity. We suggest that the best option to explore the comparison of multiple method of eLearning would be in future reviews, conducted after we have completed our current series of reviews.

Sensitivity analysis

We will consider sensitivity analyses to explore the impact of the 'Risk of bias' dimensions on the outcomes of the review. We will remove studies deemed to be at high risk of bias from the analysis, after examiningindividual study characteristics, according to the following filters:

  • studies with high risk of bias;

  • small studies (fewer than 30 participants per randomisation group);

  • source of funding (industry sponsorship, mixed sponsorship (public and industry funded, including the provision of free study material only), non-industry sponsorship (solely public funded and no provision of free material.), or not described);

  • time lapse between end of intervention and first post-test (quartiles), or last post-test;

  • studies comparing more than one internet- and LAN-based learning intervention or blended learning intervention to traditional learning. We will perform a sensitivity analysis to assess the impact of successively replacing the results of each intervention group, on the measure of effect. We will also average the mean scores for each intervention group and use this average in the meta-analysis. We will then compare the difference between the two approaches.

We will include RCTs with unclear or high risk of bias for sequence generation, if meta-analysis of these studies is feasible and appropriate. We will conduct sensitivity analyses excluding those at unclear or high risk of bias, to examine the robustness of the meta-analysis results to methodological limitations of the included studies.

Summary of findings

We will prepare a 'Summary of findings' table to present the results of the meta-analyses, based on the methods described in chapter 11 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2011). We will present a separate table for each major comparison in the review, including results of the meta-analysis for each primary outcome and potential adverse effects, as defined in the ‘Types of outcome measures’ section. We will provide a source and rationale for each assumed risk cited in the table(s). Two review authors will independently use the GRADE criteria to rank the quality of the evidence using GRADEprofiler (GRADEpro) software (Schünemann 2011). If meta-analysis is not feasible, we will present results in a narrative ‘Summary of findings’ table format, such as that used by Chan 2011 (CCCRG 2014; Chan 2011).

Acknowledgements

This review is conducted in collaboration with the World Health Organization (WHO) Department of eHealth, Knowledge Management and Sharing. We would like to thank the Cochrane Tobacco Addiction Group, in particular Dr Nicola Lindson-Hawley (Managing Editor) and Ms Lindsay Stead for their support and guidance, and the UK Cochrane Centre for their workshops in Oxford. We would also like to thank Mr Carl Gornitzki, Ms GunBrit Knutssön and Mr Klas Moberg from the University Library, Karolinska Institutet, Sweden, for developing the search strategy. We gratefully acknowledge funding from the Lee Kong Chian School of Medicine, Singapore, Nanyang Technological University, Singapore and the National Healthcare Group, Singapore, eLearning for health professionals education grant.

Appendices

Appendix 1. MEDLINE (Ovid) search strategy

1. exp education, professional/ not education, veterinary/

2. Education, Predental/

3. Education, Premedical/

4. exp Students, Health Occupations/

5. ((medic* or premedic* or dent* or laborator* or predent* or midwi?e* or nurs* or nutrition* or orthop* or podiat* or pharmac* or psycholog* or psychiatr* or health or healthcare or occupational therap* or physiotherap* or physical therap* or clinical or surg* or radiolog* or obstetric* or gyn?ecolog* or orthodont* or An?esthesi* or Dermatolog* or Oncolog* or Rheumatolog* or Neurolog* or Patholog* or P?ediatric* or Cardiolog* or Urolog*) adj3 (student* or graduate* or undergraduate* or staff or personnel or practitioner* or clerk* or fellow* or internship* or residen* or educat* or train* or novice* or tutor*)).tw,kf.

6. or/1-5

7. Computer-Assisted Instruction/

8. exp Internet/

9. Computer Simulation/

10. Patient Simulation/

11. software/

12. Mobile Applications/

13. User-Computer Interface/

14. Video Games/

15. Web Browser/

16. Education, Distance/

17. Computers/

18. exp Microcomputers/

19. exp Cell Phones/

20. Games, Experimental/

21. exp Models, Anatomic/

22. Audiovisual Aids/

23. Educational Technology/

24. Electronic Mail/

25. exp Telemedicine/

26. Telenursing/

27. Telecommunications/

28. Webcasts/

29. exp Videoconferencing/

30. ((computer* or digital* or hybrid or blended or mixed mode or distance or remote* or electronic or mobile or online* or interactiv* or multimedia or internet or web* or virtual* or game* or gaming or Videogame* or Videogaming) adj3 (classroom* or course* or educat* or instruct* or learn* or lecture* or simulat* or train* or teach* or tutor* or platform*)).tw,kf.

31. (Simulat* adj3 (course* or educat* or instruct* or learn* or train* or teach* or platform* or high-fidelity)).tw,kf.

32. e-learn*.tw,kf.

33. elearn*.tw,kf.

34. m-learn*.tw,kf.

35. mlearn*.tw,kf.

36. smartphone*.tw,kf.

37. smart-phone*.tw,kf.

38. ((mobile or cell) adj2 phone*).tw,kf.

39. iphone*.tw,kf.

40. android*.tw,kf.

41. ipad*.tw,kf.

42. Personal digital assistant*.tw,kf.

43. handheld computer*.tw,kf.

44. Mobile App?.tw,kf.

45. Mobile Application?.tw,kf.

46. webcast*.tw,kf.

47. webinar*.tw,kf.

48. flipped classroom*.tw,kf.

49. Serious game*.tw,kf.

50. Serious gaming.tw,kf.

51. Patient Simulat*.tw,kf.

52. Virtual patient*.tw,kf.

53. ((educat* or instruct* or learn* or simulat* or train* or teach* or interactiv*) adj2 technolog*).tw,kf.

54. Massive Open Online Course?.tw,kf.

55. Mooc?.tw,kf.

56. (Canvas network or Coursera or Coursesites or edx or Futurelearn or iversity or miriada x or moodle or novoed or openlearning or open2study or plato or spoc or udacity or pingpong).tw,kf.

57. or/7-56

58. 6 and 57

59. Education.fs.

60. Education/

61. Teaching/

62. Learning/

63. exp Inservice Training/

64. Curriculum/

65. educat*.tw,kf.

66. learn*.tw,kf.

67. train*.tw,kf.

68. instruct*.tw,kf.

69. teach*.tw,kf.

70. or/59-69

71. Health Personnel/

72. exp Allied Health Personnel/

73. Anatomists/

74. "Coroners and Medical Examiners"/

75. exp Dental Staff/

76. exp Dentists/

77. Health Educators/

78. Infection Control Practitioners/

79. Medical Laboratory Personnel/

80. exp Medical Staff/

81. exp Nurses/

82. exp Nursing Staff/

83. Personnel, Hospital/

84. Pharmacists/

85. exp Physicians/

86. Physician*.tw,kf.

87. Doctor*.tw,kf.

88. Nurs*.tw,kf.

89. Surg*.tw,kf.

90. Health Personnel.tw,kf.

91. healthcare professional*.tw,kf.

92. radiolog*.tw,kf.

93. dentist*.tw,kf.

94. Pharmacist*.tw,kf.

95. Hospital Administrator*.tw,kf.

96. Podiatr*.tw,kf.

97. Psycholog*.tw,kf.

98. Psychiatr*.tw,kf.

99. An?esthesi*.tw,kf.

100. Clinician*.tw,kf.

101. Dermatolog*.tw,kf.

102. General practioner*.tw,kf.

103. Cardiolog*.tw,kf.

104. Oncolog*.tw,kf.

105. Rheumatolog*.tw,kf.

106. Neurolog*.tw,kf.

107. Patholog*.tw,kf.

108. P?ediatric*.tw,kf.

109. Physiotherap*.tw,kf.

110. Physical therap*.tw,kf.

111. Occupational therap*.tw,kf.

112. dieti?ian*.tw,kf.

113. Dietetic*.tw,kf.

114. midwi?e*.tw,kf.

115. nutrition*.tw,kf.

116. orthopti*.tw,kf.

117. obstetric*.tw,kf.

118. gyn?ecolog*.tw,kf.

119. orthodont*.tw,kf.

120. Urolog*.tw,kf.

121. or/71-120

122. Health Occupations/

123. exp Allied Health Occupations/

124. Biomedical Engineering/

125. Chiropractic/

126. exp Dentistry/

127. exp Evidence-Based Practice/

128. exp Medicine/

129. exp Nursing/

130. Dietetics/

131. Optometry/

132. Orthoptics/

133. exp Pharmacology/

134. exp Pharmacy/

135. Podiatry/

136. Psychology, Medical/

137. Serology/

138. Specialization/

139. exp Surgical Procedures, Operative/

140. exp Radiography/

141. or/122-140

142. 121 or 141

143. 57 and 70 and 142

144. Psychomotor Performance/

145. motor skills/

146. ((psychomotor or procedural or technical) adj3 skill*).tw,kf.

147. (psychomotor adj3 performance).tw,kf.

148. or/144-147

149. 6 and 148

150. 58 or 143 or 149

151. limit 150 to yr="1990 -Current"

Contributions of authors

JC conceived the idea for the review. PP wrote the protocol. LTC peer-reviewed it. AH, ET, NS, LK, MJ, CL, JC provided comments on the protocol.

Declarations of interest

None known

Sources of support

Internal sources

  • The Health Services and Outcomes Research (HSOR) National Healthcare Group, Singapore.

    Salary for NS, PP and KTKL as well as infrastructure for writing the protocol

  • NTU Lee Kong Chian School of Medicine, Singapore.

    Salary of ET, MS and JC as well as infrastructure for writing the protocol and referencing

  • Imperial College London, UK.

    Salary for LTC

  • Karolinska Institutet, Sweden.

    Salary for AH, MJ and NZ

  • Implementation Science, The University of Adelaide, Adelaide, Australia.

    Salary for CL

External sources

  • No sources of support supplied


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Discipline: Information Technology

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Discipline: Information Technology

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