Inuit youth health and wellbeing programming in Canada
Bibliographic record
Abstract
Inuit youth face challenges in maintaining their wellbeing, stemming from continued impacts of colonisation. Recent work documented that urban centres, such as Winnipeg Canada, have large Inuit populations comprised of a high proportion of youth. However, youth lack culturally appropriate health and wellbeing services. This review aimed to scan peer-reviewed and grey literature on Inuit youth health and wellbeing programming in Canada. This review is to serve as an initial phase in the development of Inuit-centric youth programming for the Qanuinngitsiarutiksait program of research. Findings will support further work of this program of research, including the development of culturally congruent Inuit-youth centric programming in Winnipeg. We conducted an environmental scan and used an assessment criteria to assess the effectiveness of the identified programs. Results showed that identified programs had Inuit involvement in creation framing programming through Inuit knowledge and mostly informed by the culture as treatment approach. Evaluation of programs was diffcult to locate, and it was hard to discren between programming, pilots or explorative studies. Despite the growing urban population, more non-urban programming was found. Overall, research contributes to the development of effective strategies to enhance the health and wellbeing of Inuit youth living in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".