Promoting the strengths and resilience of an Indigenous community through photovoice
Bibliographic record
Abstract
In the face of negative media attention, community members and Elders from the First Nation community of Maskwacis identified the importance of promoting community strengths and reframing perceptions of their community. Two research questions were addressed: (1) How do youth in Maskwacis view their community strengths? and (2) To what extent can photographs be used as a tool for reframing perceptions of an Indigenous community? A community-based participatory research approach was used for the current photovoice project, through which two Elders and eleven youth were engaged. Indigenous photographers trained youth participants, and Elders mentored youth to capture photographs that represented their community's strengths and resilience. Youth selected forty photos to display at a total of six photo exhibits, where feedback was gathered from 392 attendees using brief questionnaires. The content of their photos was analyzed using thematic analysis. This resulted in four themes that described the strengths of Maskwacis, reflecting the community's strong relationships, commitment to culture, the beautiful natural world that is a part of Maskwacis, and the community's ability to look toward the future. In addition, findings provide foundational support for the use of photographs to reframe perceptions of an Indigenous community. The majority (93%) of survey respondents provided examples of ways that viewing the photos had positively changed their perceptions of Maskwacis. This study prompts consideration of the strengths and resilience of other Indigenous communities facing similar social and health issues. Therefore, findings are highly relevant to paediatric health care providers seeking to provide culturally responsive care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".