Collaborative learning of clinical skills in health professions education: the why, how, when and for whom
Bibliographic record
Abstract
OBJECTIVES: This study is designed to provide an overview of why, how, when and for whom collaborative learning of clinical skills may work in health professions education. WHY: Collaborative learning of clinical skills may influence learning positively according to the non-medical literature. Training efficiency may therefore be improved if the outcomes of collaborative learning of clinical skills are superior or equivalent to those attained through individual learning. HOW: According to a social interaction perspective, collaborative learning of clinical skills mediates its effects through social interaction, motivation, accountability and positive interdependence between learners. Motor skills learning theory suggests that positive effects rely on observational learning and action imitation, and negative effects may include decreased hands-on experience. Finally, a cognitive perspective suggests that learning is dependent on cognitive co-construction, shared knowledge and reduced cognitive load. WHEN AND FOR WHOM: The literature on the collaborative learning of clinical skills in health science education is reviewed to support or contradict the hypotheses provided by the theories outlined above. Collaborative learning of clinical skills leads to improvements in self-efficacy, confidence and performance when task processing is observable or communicable. However, the effects of collaborative learning of clinical skills may decrease over time as benefits in terms of shared cognition, scaffolding and cognitive co-construction are outweighed by reductions in hands-on experience and time on task. CONCLUSIONS: Collaborative learning of clinical skills has demonstrated promising results in the simulated setting. However, further research into how collaborative learning of clinical skills may work in clinical settings, as well as into the role of social dynamics between learners, is required.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".