532 International Journal of Circumpolar Health 70:5 2011 Research dissemination strategies in Nunatsiavut ORIGINAL ARTICLE Community-derived research dissemination strategies in an Inuit community
Bibliographic record
Abstract
Objectives. To determine how residents of the Inuit community of Nain, Nunatsiavut, Canada would like research results disseminated to their community. Study design. Qualitative study using focus groups and key informant interviews. Methods. As part of a larger study on food safety, one focus group was conducted with hunters (n=7) and a second with members of the general community (n=7) to determine research dissemination strat-egies previously used in the community, and to obtain recommendations for effective and appropriate strategies for future use. One-on-one key informant interviews were also conducted with Nain commu-nity members (n=5) selected for their insights on the study themes. Informants included a teacher, a nurse, a community elder, and one official from each of the Nain and Nunatsiavut governments. Data from focus groups and key informant interviews were combined and analysed using thematic analysis. Results. Open houses were identified as the preferred method to present research results to the commu-nity. Presentation methods should be interactive, visual and presented in both English and Inuktitut. Research dissemination efforts should be timely and involve both the researcher and a local official or community member to give the results additional validity and relevance. If possible, involving youth in
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".