Defining generic skills to better support the development of future health professionals: results from a scoping review
Bibliographic record
Abstract
Over the past two decades, there has been a growing interest in the role of universities in developing generic skills, in addition to disciplinary ones, to help students adapt to a changing workplace. The need to develop these skills is particularly critical for healthcare students who must face challenges in health systems. This scoping study, following Arksey and O'Malley's ([2005]. Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616) method provides an overview of the scientific literature on generic skills in health studies. The definitions, categorizations, and measurement tools from 43 articles were mapped and summarized and gaps were identified. Definitions and terms were varied but overall, generic skills are considered cross-disciplinary and cross-professional. Nevertheless, the term skill itself was not defined. The lack of consensus on categorization and assessment led us to propose a typology to group all the skills mentioned into four categories: personal, ideas and object-related, interpersonal and community-related skills. One conclusion is that generic skills are valued foremost in relation to labor market demands with an emphasis on skills such as knowledge, communication, and planning over ethical and citizenship skills that are essential for future healthcare professionals. It is imperative to consider generic skills that are necessary to face the complex challenges of today’s world.
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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 | 0.061 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".