Beyond deficit: ‘strengths‐based approaches’ in Indigenous health research
Bibliographic record
Abstract
Health research concerning Indigenous peoples has been strongly characterised by deficit discourse-a 'mode of thinking' that is overly focused on risk behaviours and problems. Strengths-based approaches offer a different perspective by promoting a set of values that recognise the capacities and capabilities of Indigenous peoples. In this article, we seek to understand the conceptual basis of strengths-based approaches as currently presented in health research. We propose that three main approaches exist: 'resilience' approaches concerned with the personal skills of individuals; 'social-ecological' approaches, which focus on the individual, community and structural aspects of a person's environment; and 'sociocultural' approaches, which view 'strengths' as social relations, collective identities and practices. We suggest that neither 'resilience' nor 'social-ecological' approaches sufficiently problematise deficit discourse because they remain largely informed by Western concepts of individualised rationality and, as a result, rest on logics that support notions of absence and deficit. In contrast, sociocultural approaches tend to view 'strengths' not as qualities possessed by individuals, but as the structure and character of social relations, collective practices and identities. As such, they are better able to capture Indigenous ways of knowing and being and provide a stronger basis on which to build meaningful interventions.
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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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".