Developing generic skills for future health professionals: Student and faculty perceptions of a Recovery College curriculum and courses
Bibliographic record
Abstract
Abstract As healthcare systems worldwide grapple with complex challenges such as limited resources, qualified personnel shortages, and rapid technological advancements, there is an urgent need for educational transformation in healthcare professions. This urgency arises from the necessity for healthcare professionals to evolve beyond traditional roles and acquire essential generic skills such as adaptive, epistemic, relational, ethical, and citizenship skills - areas identified as gaps in conventional university curricula. This study investigates the potential of the Recovery College model, integrated into a Canadian university's healthcare curriculum, to address these gaps. Through qualitative group interviews with eight students and three faculty members, and subsequent descriptive content analysis, we explored the perceived outcomes of this model. We discerned 15 themes within the five core categories of generic skills (Epistemic, Ethical, Relational, Adaptative, and Citizenship skills), with "experiential knowledge acquisition" central to the training input, and other significant themes including "ethical sensitivity", "collaborative communication", "self-care", and "open-mindedness to diversity". Our findings highlight the Recovery College model's potential in fostering these crucial skills among future healthcare professionals and challenging prevailing epistemic injustices in healthcare. Further investigations are needed to understand the long-term effects of this model on healthcare practice and to explore its potential integration into wider healthcare education programs.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".