Collaborative learning processes in the context of a public health professional development program: a case study
Bibliographic record
Abstract
The health promotion laboratory (HPL – Canada) is a public health professional development program building on a collaborative learning approach in order to support long-term practice change in local health services teams. This study aims to analyse the collaborative learning processes of two teams involved in the program during the first year of implementation. Based on a multiple case study design involving observations, interviews, and documentary sources, the study: (1) describes the learning process by which each team built a common understanding of the problem at hand and developed an intervention to address it; (2) identifies factors that facilitated or hindered these processes; and (3) proposes a cross-case explanation of the collaborative learning process in the HPL. The results demonstrate that the two teams learned by expanding their repertoire of actions, albeit experiencing different processes. Results point to the central role of shared mental models and key influencing factors, such as commitment and participation (team cohesion), team climate (psychological safety), and leadership style. Unlike previous studies on team learning that concentrated on existing teams in organisations, the current research studied purposely created teams working at transforming their practices and showed that they can successfully learn if specific conditions are achieved.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".