Nutritionists as policy advocates: the case of obesity prevention in Quebec, Canada
Bibliographic record
Abstract
Abstract Objective: A core function of the public health nutrition workforce is advocacy. Little is known of the nutritionists’ role in policymaking from a policy process theory perspective. The current study analyses the nutritionists’ role in advocating for a six-year governmental plan on obesity prevention in Quebec, Canada. Design: We conducted qualitative research using Quebec’s obesity policy as a case study to understand the role of nutritionists in advocating for obesity prevention policies. A conceptual framework combining the Advocacy Coalition Framework with a political analysis model based on the Theory of the Strategic Actor was developed to analyse the beliefs, interests and strategies of policy actors including nutritionists. Data sources comprised semi-structured open-ended interviews with key policy actors (n 25), including eight nutritionists (32 %) and policy-related documents (n 267). Data analysis involved thematic coding and analysis using NVivo 11 Pro. Setting: Quebec, Canada. Participants: Key policy actors including nutritionists. Results: Nutritionists formed the core of the dominant public health coalition. They advocated for an inter-sectoral governmental plan to prevent obesity through enabling environments. Their advocacy, developed through an iterative process, comprised creating a think tank and reinforcing partnerships with key policy actors, conducting research and developing evidence, communicating policy positions and advocacy materials, participating in deliberative forums and negotiating an agreement with other coalitions in the policy subsystem. Conclusions: Nutritionists’ advocacy influenced agenda setting and policy formulation. This research may contribute to empowering the public health nutrition workforce and strengthening its advocacy practices. It informs practitioners and researchers concerned with obesity policy and workforce development.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.046 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".