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Record W3039377463 · doi:10.1017/s1368980020001433

Strengthening public health nutrition: findings from a situational assessment to inform system-wide capacity building in Ontario, Canada

2020· article· en· W3039377463 on OpenAlexaffabout
Rachel Prowse, Sarah A. Richmond, Sarah Carsley, Heather Manson, Brent Moloughney

Bibliographic record

VenuePublic Health Nutrition · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of WaterlooPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPublic healthSnowball samplingSituational ethicsPublic relationsCapacity buildingPsychological interventionPopulation healthEnvironmental healthMedicineFocus groupMedical educationPsychologyBusinessNursingPolitical scienceMarketing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0160.005
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.139
GPT teacher head0.398
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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