Strengthening public health nutrition: findings from a situational assessment to inform system-wide capacity building in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To assess public health nutrition practice within the public health system in Ontario, Canada to identify provincial-wide needs for scientific and technical support. DESIGN: A qualitative descriptive study was conducted to identify activities, strengths, challenges and opportunities in public health nutrition practice using semi-structured key informant interviews (n 21) and focus groups (n 10). Recorded notes were analysed concurrently with data generation using content analysis. System needs were prioritised through a survey. SETTING: Public health units. PARTICIPANTS: Eighty-nine practitioners, managers, directors, medical officers of health, researchers and other stakeholders were purposively recruited through snowball and extreme case sampling. RESULTS: Five themes were generated: (i) current public health nutrition practice was broad, complex, in transition and collaborative; (ii) data/evidence/research relevant to public health needs were insufficiently available and accessible; (iii) the amount and specificity of guidance/leadership was perceived to be mismatched with strong evidence that diet is a risk factor for poor health; (iv) resources/capacity were varied but insufficient and (v) understanding of nutrition expertise in public health among colleagues, leadership and other organisations can be improved. Top ranked needs were increased understanding, visibility and prioritisation of healthy eating and food environments; improved access to data and evidence; improved collaboration and coordination; and increased alignment of activities and goals. CONCLUSIONS: Collective capacity in the public health nutrition can be improved through strategic system-wide capacity-building interventions. Research is needed to explore how improvements in data, evidence and local contexts can bridge research and practice to effectively and efficiently improve population diets and health.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".