Scientific rigour and innovations in participatory action research investigating workplace learning in continuing interprofessional education
Bibliographic record
Abstract
The persistent theory-practice gap shows how challenging it can be for healthcare professionals to keep updating their practices. The continuing education challenges are partly explained by the tremendous stream of new discoveries in health and the epidemic of multi-morbid conditions. Participatory action research (PAR) is used in healthcare as a research approach that capitalizes on people's resources to better understand and enhance their professional practices. PAR thus can consolidate our knowledge on workplace learning in continuing interprofessional education while directly improving quality of care. However, PAR lacks clear scientific criteria to ensure the consistency between the investigators' methodology and philosophy, which jeopardize its credibility. This paper outlines the principles of rigour in PAR and describes the additions of a preliminary planning phase to Kemmis and McTaggart's PAR description as well as the use of the professional co-development group, an action-oriented data collection method. We believe that this will help PAR co-participants achieve improved scientific rigour and encourage more investigators to collaborate through this research approach contributing to the advancement of knowledge on workplace learning in continuing interprofessional education.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".