Guiding health promotion efforts with urban Inuit: a community-specific perspective on health information sources and dissemination strategies.
Bibliographic record
Abstract
OBJECTIVE: To develop a community-specific perspective of health information sources and dissemination strategies of urban Inuit to better guide health promotion efforts. METHODS: Through a collaborative partnership with the Tungasuvvingat Inuit Family Resource Centre, a series of key informant interviews and focus groups were conducted to gather information on specific sources of health information, strategies of health information dissemination, and overall themes in health information processes. FINDINGS: Distinct patterns of health information sources and dissemination strategies emerged from the data. Major themes included: the importance of visual learning, community Elders, and cultural interpreters; community cohesion; and the Inuit and non-Inuit distinction. The core sources of health information are family members and sources from within the Inuit community. The principal dissemination strategy for health information was direct communication, either through one-on-one interactions or in groups. CONCLUSION: This community-specific perspective of health information sources and dissemination strategies shows substantial differences from current mainstream models of health promotion and knowledge translation. Health promotion efforts need to acknowledge the distinct health information processes of this community, and should strive to integrate existing health information sources and strategies of dissemination with those of the community.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".