Pan-Arctic TV Series on Inuit wellness: a northern model of communication for social change?
Bibliographic record
Abstract
OBJECTIVES: This paper provides highlights of a utilization-focused evaluation of a collaborative Pan-Arctic Inuit Wellness TV Series that was broadcast live in Alaska and Canada in May 2009. This International Polar Year (IPY) communication and outreach project intended to (1) share information on International Polar Year research progress, disseminate findings and explore questions with Inuit in Alaska, Canada and Greenland; (2) provide a forum for Inuit in Alaska, Canada and Greenland to showcase innovative health and wellness projects; (3) ensure Inuit youth and adult engagement throughout; and (4) document and reflect on the overall experience for the purposes of developing and "testing" a participatory communication model. STUDY DESIGN: Utilization-focused formative evaluation of the project, with a focus on overall objectives, key messages and lessons learned to facilitate program improvement. METHODS: Participant observation, surveys, key informant interviews, document review and website tracking. RESULTS: Promising community programs related to 3 themes - men's wellness, maternity care and youth resilience - in diverse circumpolar regions were highlighted, as were current and stillevolving findings from ongoing Arctic research. Multiple media methods were used to effectively deliver and receive key messages determined by both community and academic experts. Local capacity and new regional networks were strengthened. Evidence-based resources for health education and community action were archived in digital formats (websites and DVDs), increasing accessibility to otherwise isolated individuals and remote communities. CONCLUSIONS: The Pan-Arctic Inuit Wellness TV Series was an innovative, multi-dimensional communication project that raised both interest and awareness about complex health conditions in the North and stimulated community dialogue and potential for increased collaborative action. Consistent with a communication for social change approach, the project created new networks, increased motivation to act and provided new tools to do so, and increased local community involvement and "voice" in the discussion and dissemination of successful strategies to promote Inuit wellness.
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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 | 0.007 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".