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Record W3111994329 · doi:10.1177/0840470420967705

Setting the standard for healthy eating: Continuous quality improvement for health promotion at Nova Scotia Health

2020· article· en· W3111994329 on OpenAlexafffundabout
Laura J. Kennedy, Nathan Taylor, Taylor Nicholson, Emily Jago, Brenda MacDonald, Catherine L. Mah

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of TorontoNova Scotia Health AuthorityDalhousie University
FundersFaculty of Graduate Studies, Dalhousie UniversityResearch Nova ScotiaCanada Research Chairs
KeywordsNova scotiaStatus quoHealthy eatingHealth promotionBenchmark (surveying)Healthy foodQuality (philosophy)Quality managementPromotion (chess)Health policyMedicinePsychologyBusinessGerontologyNursingPolitical scienceMarketingSociologyPublic healthPhysical therapyGeographyFood science

Abstract

fetched live from OpenAlex

Healthcare organizations engage in continuous quality improvement to improve performance and value-for-performance, but the pathway to change is often rooted in challenging the way things are "normally" done. In an effort to propel system-wide change to support healthy eating, Nova Scotia Health developed and implemented a healthy eating policy as a benchmark to create a food environment supportive of health. This article describes the healthy eating policy and its role as a benchmark in the quality improvement process. The policy, rooted in health promotion, sets a standard for healthy eating and applies to stakeholders both inside and outside of health. We explain how the policy offers nutrition but also cultural benchmarks around healthy eating, bringing practitioners throughout Nova Scotia Health together and sustaining collaborative efforts to improve upon the status quo.

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)0.014
metaresearch head score (Gemma)0.028
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: none
Teacher disagreement score0.928
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0090.002
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.105
GPT teacher head0.477
Teacher spread0.372 · 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

Citations2
Published2020
Admission routes3
Has abstractyes

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