Evaluation of remote learning in knowledge translation through massive open online courses in the DRC
Bibliographic record
Abstract
Background: Building capacity in knowledge translation is necessary to bridge the gap between research and practice, but evaluation of knowledge translation training initiatives is limited. In the Democratic Republic of Congo (DRC), stakeholders of a public health project participated in such training that involved completing two massive open online courses (MOOCs) autonomously, with biweekly coaching by a trainer and feedback through email. Objectives: This qualitative study aims to report on their experience with distance training, including implementation barriers and facilitators and the effect on their practice. Method: Twelve semi-structured interviews were conducted with trainees 8 months post-training. The evaluation used three levels of Kirkpatrick’s model – reactions, learning, behaviour change – and the TIPEC framework to identify barriers to implementation. Results: Participants faced significant technological obstacles. Nevertheless, they described a satisfying and collaborative learning experience. The training topic was deemed relevant. The biweekly coaching they received was appreciated, though most did not consider it essential. Most trainees had put their learning into practice by the time of the evaluation. Conclusion: In a context of limited resources, MOOC-based knowledge translation training met the needs of these professionals spread out across a vast territory and was deemed effective from an individual standpoint. This study confirms the importance of tailoring the training to learners’ professional contexts in the DRC. Contribution: This study assessed the effectiveness of MOOC-based learning in a little-described context of a French-speaking low- and middle-income country. It contributes to identifying the added value of this training method.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".