Seeing people, not patients: a strength-based approach to health and healing through asset mapping
Bibliographic record
Abstract
A deficits model is the cornerstone for health professions education, practice and scholarship. Focusing on diseases, addictions, social risk factors, and other deficits leads us to see medicalized beings with problems, also known as “patients”. This approach does particular harm to those who come from marginalized communities, groups who have always been viewed through the deficits lens by their colonizers/oppressors. When we focus on understanding strengths—including culture, language, resilience, skills, and resources we can begin to see "people" as full human beings possessing the tools necessary for their own healing. This approach, also known as “asset mapping”, will allow us to provide more effective, decolonized care, and will open the door to deeper healing for all involved.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".