Information and communication technology to enhance continuing professional development (CPD) and continuing medical education (CME) for Rwanda: a scoping review of reviews
Bibliographic record
Abstract
BACKGROUND: Access to high quality continuing professional development (CPD) is necessary for healthcare professionals to retain competency within the ever-evolving worlds of medicine and health. Most low- and middle-income countries, including Rwanda, have a critical shortage of healthcare professionals and limited access to CPD opportunities. This study scoped the literature using review articles related to the use of information and communication technology (ICT) and video conferencing for the delivery of CPD to healthcare professionals. The goal was to inform decision-makers of relevant and suitable approaches for a low-income country such as Rwanda. METHODS: PubMed and hand searching was used. Only review articles written in English, published between 2010 and 2019, and reporting the use of ICT for CPD were included. RESULTS: Six review articles were included in this study. Various delivery modes (face to face, pure elearning and blended learning) and technology approaches (Internet-based and non-Internet based) were reported. All types of technology approach enhanced knowledge, skills and attitudes. Pure elearning is comparable to face-to-face delivery and better than 'no intervention', and blended learning showed mixed results compared to traditional face-to-face learning. Participant satisfaction was attributed to ease of use, easy access and interactive content. CONCLUSION: The use of technology to enhance CPD delivery is acceptable with most technology approaches improving knowledge, skills and attitude. For the intervention to work effectively, CPD courses must be well designed: needs-based, based on sound educational theories, interactive, easy to access, and affordable. Participants must possess the required devices and technological literacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".