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 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 | 0.597 | 0.585 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.011 | 0.131 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.013 | 0.014 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".