Distance education for tobacco reduction with Inuit frontline health workers
Bibliographic record
Abstract
BACKGROUND: Tobacco reduction is a major priority in Canadian Inuit communities. However, many Inuit frontline health workers lacked the knowledge, confidence and support to address the tobacco epidemic. Given vast distances, high costs of face-to-face training and previous successful pilots using distance education, this method was chosen for a national tobacco reduction course. OBJECTIVE: To provide distance education about tobacco reduction to at least 25 frontline health workers from all Inuit regions of Canada. DESIGN: Promising practices globally were assessed in a literature survey. The National Inuit Tobacco Task Group guided the project. Participants were selected from across Inuit Nunangat. They chose a focus from a "menu" of 6 course options, completed a pre-test to assess individual learning needs and chose which community project(s) to complete. Course materials were mailed, and trainers provided intensive, individualized support through telephone, fax and e-mail. The course ended with an open-book post-test. Follow-up support continued for several months post-training. RESULTS: Of the 30 participants, 27 (90%) completed the course. The mean pre-test score was 72% (range: 38-98%). As the post-test was done using open books, everyone scored 100%, with a mean improvement of 28% (range: 2-62%). CONCLUSIONS: Although it was often challenging to contact participants through phone, a distance education approach was very practical in a northern context. Learning is more concrete when it happens in a real-life context. As long as adequate support is provided, we recommend individualized distance education to others working in circumpolar regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".