Participatory methods for Inuit public health promotion and programme evaluation in Nunatsiavut, Canada
Bibliographic record
Abstract
Engaging stakeholders is crucial for health promotion and programme evaluations; understanding how to best engage stakeholders is less clear, especially within Indigenous communities. The objectives of this thesis research were to use participatory methods to: (1) co-develop and evaluate a whiteboard video for use as a public health promotion tool in Rigolet, Nunatsiavut, and (2) develop and validate a framework for participatory evaluation of Inuit public health initiatives in Nunatsiavut, Labrador. Data collection tools included interactive workshops, community events, interviews, focus-group discussions and surveys. Results indicated the whiteboard video was an engaging and suitable medium for sharing public health messaging due to its contextually relevant elements. Participants identified 4 foundational evaluation framework components necessary to conduct appropriate evaluations, including: (1) community engagement, (2) collaborative evaluation development, (3) tailored evaluation data collection and (4) evaluation scope. This research illustrates stakeholder participation is critical to develop and evaluate contextually relevant public health initiatives in Nunatsiavut, Labrador and should be considered in other Indigenous communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.182 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".