The Impact of Indigenous Youth Sharing Digital Stories About HIV Activism
Bibliographic record
Abstract
INTRODUCTION: This article reports on the micro-, meso-, and macro-level impacts of sharing digital stories created by Indigenous youth leaders about HIV prevention activism in Canada. METHOD: Eighteen participants created digital stories and hosted screenings in their own communities to foster dialogue. Data for this article are drawn from individual semistructured interviews with the youth leaders, audio-recordings of audience reflections, and research team member's field notes collected between 2012 and 2015 across Canada. Data were coded using NVivo. A content analysis approach guided analysis. RESULTS: The process of sharing their digital stories had a positive impact on the youth themselves and their communities. Stories also reached policymakers. They challenged conventional public health messaging by situating HIV in the context of Indigenous holistic conceptions of health. DISCUSSION: The impact(s) of sharing digital stories were felt most strongly by their creators but rippled out to create waves of change for many touched by them. More research is warranted to examine the ways that the products of participatory visual methodologies can be powerful tools in creating social change and reducing health disparities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".