Bibliographic record
Abstract
OBJECTIVE: To determine what health problems concern youth in the Canadian North and what solutions these youths propose to address these problems by interviewing Inuvik youths, using their photographs to spark discussion. DESIGN: Qualitative study and photo-novella technique. SETTING: Inuvik, NWT, from July 1 to August 31, 2004. PARTICIPANTS: Thirty-five youths from Inuvik between the ages of 10 and 22. Two boys and 2 girls between the ages of 17 and 22 from the Inuvialuit and Gwich'in cultures featured in an educational video developed from the study. METHOD: Disposable cameras were distributed to 35 youths; interviews structured around the photographs were recorded with 14 youths. Thematic analysis of the interview transcripts was completed; the themes identified formed the basis of a 19-minute video featuring 4 of these youths. MAIN FINDINGS: Themes developed around mothers, culture, the land, and boredom. Specific health concerns identified were smoking, alcohol and drug abuse, and teen pregnancy. Solutions suggested included sources of recreation and distraction from substance abuse, such as a movie theatre, a shopping mall, and upgrades to the skatepark. CONCLUSION: By having Inuvik youths share their stories and perspectives, health care providers can gain insight into the issues and concerns of youth in northern communities and expand their capacity to heal. Photo novella is a useful method for research in adolescent health.
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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 | 0.001 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".