Acknowledging a Holistic Framework for Learner Wellness: The Human Capabilities Approach
Bibliographic record
Abstract
To the Editor: We commend Gengoux and Roberts’ recent Invited Commentary1 for raising important issues about student mental health and wellness. Wellness programs clearly need to be evidence- based and tailored to meet individual learner needs and circumstances. They also need to be respectful of issues arising from intersections of—among other facets of identity—race, culture, socioeconomic status, and gender in the context of medical education training. We also agree that this is not just a matter of respecting identities and legitimate differences in the human condition, it is about actively challenging social stigma and the tendency to reduce others to a single negatively framed characteristic that condemns them to a socially excluded and pilloried class. In response to the “epidemic of burnout”2 in medicine, wellness initiatives at our institution, the Cumming School of Medicine, are increasingly focusing on early prevention and intervention through engagement, advocacy, and scholarship. Wellness depends, we believe, on a core principle of embracing individual differences and vulnerability. If we recognize that everyone has abilities and disabilities, everyone is unique, there is no superordinate class or characteristic, and anyone can struggle with issues arising from their circumstances, then we can begin to address wellness at a more fundamental systems level. To that end, we draw on Nussbaum’s human capabilities approach,3 which is based on the principle that the freedom to achieve well-being is of primary moral importance, and . . . that freedom to achieve well-being is to be understood in terms of people’s capabilities, that is, their real opportunities to do and be what they have reason to value.3 By attending to opportunity as well as competence, we aim to orient and integrate wellness initiatives and programing and the scholarship we build around them. This approach is central to the Wellness Innovation Scholarship for Health Professions Education and Health Sciences (WISHES) laboratory at our institution. WISHES is taking a holistic approach that focuses on areas of wellness, such as mental, physical, occupational, social, and intellectual domains for individual learners and teachers; health professions education/training programs; and the intersection of the higher education system and the health care system.4 By using a human capabilities approach, we consider the interplay between competence and opportunity when addressing issues associated with wellness, and by doing so, we are seeking to have a positive impact not just on the individuals that make up our community but on the systems that influence wellness for all. Aliya Kassam, PhDAssistant professor, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected]Rachel Ellaway, PhDProfessor, Department of Community Health Sciences, and director, Office of Health and Medical Education Scholarship (OHMES), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.035 | 0.073 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".