Identifying and Applying a Strength-Based Research Approach in Indigenous Health
Bibliographic record
Abstract
Strength-based approaches are regarded as being effective in research with communities but there are minimal examples of application as a research approach, particularly in health, with Indigenous populations, and with youth. Systemic racism manifested through colonization, including in academic institutions, has contributed to historical and ongoing traumas by supporting the overuse of deficit-based approaches with Indigenous populations in many disciplines, including health. We present our creation and application of a strength-based methodological approach to research in health alongside Dene First Nation youth. It was developed on principles consistent with the strength-based paradigms in social work, psychology, and education. We share four main components of strength-based research approach: 1) identifying strengths, 2) prioritizing and creating descriptions of strengths, 3) refining strengths by gathering contextual examples, and 4) depicting strengths to plan future research. Each component included qualitative methods (such as asset-mapping, nominal group technique, storytelling interviews, and participatory 360-degree video) that reflected aspects of strength-based approaches and emphasized active participation, multiple knowledge sources, and empowerment. Utilizing this approach promoted connectivity to the larger environment, inclusivity of multi-knowledge sources, agency and voice, increased empowerment, and practicality in action. We share challenges and lessons learned from exploring a strength-based approach in health research and provide insights for researchers interested in applying a strength-based approach to research, particularly in Indigenous health.
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 | 0.194 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.003 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".