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Record W4384024217 · doi:10.1080/07448481.2023.2227725

The campus food environment and postsecondary student diet: a systematic review

2023· review· en· W4384024217 on OpenAlexafffund
Olivia Caruso, Schaafsma Holly, Louise W. McEachern, Jason A. Gilliland

Bibliographic record

VenueJournal of American College Health · 2023
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsLawson Health Research InstituteChildren’s Health Research InstituteWestern University
FundersCanadian Institutes of Health Research
KeywordsUniversity campusEnvironmental healthPurchasingPsychologyMedicineMarketingBusiness

Abstract

fetched live from OpenAlex

Objective: Examine the impact of the campus food environment on postsecondary students’ dietary behaviors (e.g., dietary intake) and food purchasing. Participants: Students currently attending a postsecondary institution, all ages and geographic locations included. Methods: A systematic search from January 2000-October 2022 was conducted in six databases using postsecondary education, food environment, and diet related keywords. Results: In total, 25 quantitative and 10 qualitative studies were extracted. All quantitative studies that conducted statistical analyses (n = 15) reported a statistically significant relationship between the campus food environment and dietary intake, including both positive and negative effects. All qualitative studies (n = 10) discussed students’ experience of the campus food environment influencing their diet. Conclusions: This review found moderate evidence that the campus food environment has an impact on postsecondary students’ dietary behaviors. A campus environment that has healthy foods accessible, affordable, and acceptable for postsecondary students may have a beneficial impact on students’ dietary intake.

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)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.358
Teacher spread0.323 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2023
Admission routes2
Has abstractyes

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