MOOC-based blended learning for knowledge translation capacity-building: A qualitative evaluative study
Bibliographic record
Abstract
This qualitative study investigated the effectiveness of blended learning using MOOCs (massive open online courses) for capacity-building in knowledge translation (KT). The evaluation followed Kirkpatrick's updated model. A total of 23 semi-directed interviews were conducted with participants working at a research centre in Côte d'Ivoire, with a first wave of interviews immediately post-training and a second wave after five months. Results showed that the training met learners' needs, with both the content and teaching format being deemed appropriate. Learners reacted positively to face-to-face activities and affirmed the importance of coaching for putting learning into practice. Specific KT skills and principles appeared to have been acquired, such as a procedure for structuring the KT process and improved skills for communicating and presenting scientific knowledge. Five months after the training, encouraging changes were reported, but the sustainability of the new KT practices remained uncertain. KT capacity-building initiatives in low- and middle-income countries struggle to meet demand. Little is known about effective KT training in that context, and even less in non-anglophone countries. The study presented here contributes to the understanding of success factors from the learners' standpoint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".