Indigenous strengths-based approaches to healthcare and health professions education – Recognising the value of Elders’ teachings
Bibliographic record
Abstract
Background: A strengths-based lens is essential for the pursuit of health equity among Indigenous populations. However, health professionals are often taught and supported in practice via deficit-based approaches that perpetuate inequity for Indigenous peoples. Deficit narratives in healthcare and health education are reproduced through practices and policies that ignore Indigenous strengths, disregard human rights, and reproduce structural inequalities. When strengths are recognised it is possible to build capacities and address challenges, while not losing sight of the structural factors impacting Indigenous peoples' health. Objective: In this paper, we examine Indigenous strengths-based approaches to policy and practice in healthcare and health professions education when delivered alongside teachings shared by Elders from the Cree, Blackfoot and Métis Nations of Alberta, Canada. Method: Literature and Elders' teachings were used to shift strengths-based approaches from Western descriptions of what might be done, to concrete actions aligned with Indigenous ways. Results: Four pointers for future action adopting a strengths-based approach are identified: enacting gifts - focusing on positive attributes; upholding relationality - centring good relationships; honouring legacy - restoring self-determination; and reconciling truth - attending to structural determinants of health. Conclusion: Identified directions and actionable strategies offer a promising means to advance Indigenous health equity through strengths-based actions that change existing narratives and advance health equity.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".