Developing an alcohol strategy for the Northwest Territories: Evaluating global research evidence against rural and remote realities
Bibliographic record
Abstract
OBJECTIVES: This paper outlines the engagement process that was used to develop the Northwest Territories Alcohol Strategy, based on a recommendation by the developers of the Canadian Alcohol Policy Evaluation report, and how this informed the final actions in the strategy. METHODS: A literature review, four targeted engagement activities, and iterative validation by advisory groups and community and Indigenous leadership were used to evaluate, modify, or reject the original recommendations and develop the final actions that were included in the NWT Alcohol Strategy. RESULTS: There are fourteen original CAPE recommendations, four of which had already been implemented in the Northwest Territories before the development of the strategy. On completion of the process, four recommendations had already been implemented in the NWT. Two recommendations were included in the strategy without changes, two were adapted for use in the strategy, and six were not included. One stand-alone alcohol policy measure was created and included. CONCLUSION: Alcohol strategies are dependent on a variety of contextual factors. Developers need to take into consideration the unique geography, political climate, and cultural context of the region for which they are being developed, in order to produce a strategy that is applicable, acceptable, and feasible at the community level.
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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 | 0.255 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".