Developing generic skills for future health professionals: student and faculty perceptions of a recovery college curriculum and courses
Bibliographic record
Abstract
Purpose As health-care 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 health-care professions. This urgency arises from the necessity for health-care professionals to evolve beyond traditional roles and acquire essential generic skills such as adaptative, epistemic, relational, ethical and citizenship skills – areas identified as gaps in conventional university curricula. This study aims to investigate the potential of the recovery college (RC) model, integrated into a Canadian university’s health-care curriculum, to address these gaps. Design/methodology/approach Through qualitative group interviews with eight students and three faculty members and subsequent descriptive content analysis, the authors explored the perceived outcomes of this model. Findings The authors 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.” The findings highlight the RC model’s potential in fostering these crucial skills among future health-care professionals and challenging prevailing epistemic injustices in health care. Research limitations/implications Further investigations are needed to understand the long-term effects of this model on health-care practice and to explore its potential integration into wider health-care education programs. Originality/value This study enriches understanding of the RC model’s role in health-care education, thereby proposing a significant shift toward more inclusive and effective health-care professional training.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".