Insights from the evaluation of a provincial healthy eating strategy in Nova Scotia, Canada
Bibliographic record
Abstract
OBJECTIVE: Healthy Eating Nova Scotia represents the first provincial comprehensive healthy eating strategy in Canada and a strategy that is framed within a population-health model. Five years after strategy launch, our objective was to evaluate Healthy Eating Nova Scotia to determine perceptions of strategy implementation and strategy outputs. The focus of the current paper is on the findings of this evaluation. DESIGN: We conducted an evaluation of the strategy through three activities that included a document review, survey of key stakeholders and in-depth interviews with key strategy informants. The findings from each of the activities were integrated to determine what has worked well with strategy implementation, what could be improved and what outputs have resulted. SETTING: The evaluation was conducted in the Canadian province of Nova Scotia. PARTICIPANTS: Participants for this evaluation included survey respondents (n 120) and key informants (n 16). A total of 156 documents were also reviewed. RESULTS: Significant investments have been made towards inter-sectoral partnerships and resourcing that has provided the necessary leadership and momentum for the strategy. Policy development has been leveraged through the strategy primarily in the health and education sectors and is perceived as a visible success. Clarity of human resource roles and funding within the context of a provincial strategy may be beneficial for continued strategy implementation, as is expansion of policy development. CONCLUSIONS: Known to be the first evaluation of its kind, these findings and related considerations will be of interest to policy makers developing and implementing similar strategies in their own jurisdictions.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".