Exploring Nunavut Public Health System’s Readiness to Implement Obesity Prevention Policies and Programs in the Canadian Arctic
Bibliographic record
Abstract
Background. Rapid changes in the food and built environments in the Canadian Arctic have contributed to a dramatic increase in the prevalence rates of obesity. The objective of this study was to explore the determinants of Nunavut public health system’s commitment to implement obesity prevention policies and programs in the territory to reduce the burden of obesity-related diseases.Methods. In total, 93 program managers, program officers, and policy analysts who are responsible for program and policy development and implementation within the Nunavut Department of Health (NDH) were asked to complete the validated Organizational Readiness for Implementing Change (ORIC) questionnaire. Organization-level readiness (commitment) was determined based on aggregated individual-level data using bivariate correlations and multivariate linear regression analyses.Results. Of the 93 questionnaires that were distributed only 67 (72%) were returned fully completed. Organization-level commitment to implement obesity prevention policies and programs was low. Only 2.9% of respondents strongly agreed that NDH was committed to implementing obesity prevention policies and programs. The study showed a strong positive correlation between NDH’s commitment and perceived value (r = .73), perceived efficacy (r = .50), and resource availability (r = .25). There was no correlation between commitment and knowledge. In the multivariate linear regression model, perceived value was the only significant predictor of NDH’s commitment to implement obesity prevention policies and programs (β= 0.66).Conclusions. Successful adoption and implementation of obesity prevention policies and programs in the Canadian Arctic largely depend on the perception of value and benefits of and belief in the change efforts among employees of the Nunavut Department of Health. Convincing policy makers of the value of preventive policies and programs is an important and necessary first step towards decreasing the prevalence of obesity in the Inuit population.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".