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Record W2895246255 · doi:10.1002/fsn3.814

Evaluation of actions, barriers, and facilitators to reducing dietary sodium in health care institutions

2018· article· en· W2895246255 on OpenAlexaffabout
Michael J. Lacey, Sharon Chandra, Roula Tzianetas, JoAnne Arcand

Bibliographic record

VenueFood Science & Nutrition · 2018
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsSt Joseph's Health CentreOntario Tech UniversityToronto Public Health
Fundersnot available
KeywordsBusinessHealth careNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, population-wide sodium reduction strategies have been adopted and implemented to address the adverse health effects of excess dietary sodium. However, in Canada, minimal coordinated action by governments has occurred, including interventions aimed at food service operations in hospitals and long-term care (LTC) centers. The objective of this study was to investigate actions, attitudes, barriers, and facilitators related to sodium reduction in these institutions. METHODOLOGY: A cross-sectional survey was administered to food service administrators working in hospitals and LTC facilities in Ontario. Responses from key informants from 27 institutions, representing 9,823 patient/resident beds were included. RESULTS: Overall, 63.0% of institutions had an established sodium target (900-4,000 mg/day). The reported sodium level on "regular" menus was 2,845 ± 1,025 mg/day. Sixty-three percent believed it was important to reduce sodium on inpatient/resident menus. Top facilitators reported for sodium reduction included group purchasing organizations identifying lower sodium foods (85.2%), increased availability of pre-packaged lower sodium products (77.8%), government prioritizing and providing support and resources (74.1%), and improved taste of lower sodium foods (74.1%). Only 37.0% believed that patient/resident satisfaction would decrease with sodium reduction. Sodium reduction practices were variable among food service operations. CONCLUSIONS: These data support the need for consistent and coordinated policies to facilitate sodium reduction in hospitals and long-term care settings and for multi-sectorial government, industry, and institutional support to ensure success.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.496
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.403
Teacher spread0.295 · 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 teacher head, 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
Published2018
Admission routes2
Has abstractyes

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