MétaCan
Menu
Back to cohort
Record W2414659226 · doi:10.2196/publichealth.5166

Availability and Accessibility of Student-Specific Weight Loss Programs and Other Risk Prevention Health Services on College Campuses

2016· article· en· W2414659226 on OpenAlexvenueno aff
Sarah Lynch, Sharon Hayes, Melissa A. Napolitano, Katrina Hufnagel

Bibliographic record

VenueJMIR Public Health and Surveillance · 2016
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightWeight lossMedical educationEnvironmental healthMedicinePsychologyGerontologyObesity

Abstract

fetched live from OpenAlex

BACKGROUND: More than one third of college students who are overweight or obese are in need of weight loss programs tailored to college students. However, the availability and accessibility of these programs is unknown. OBJECTIVE: The aim of this study is to examine the availability and ease of access to weight loss programs for students at 10 universities with the largest undergraduate enrollment. METHODS: The 10 public universities with the largest student bodies with a mean (SD) undergraduate enrollment of 41,122 (7657) students were examined. The websites of the universities were assessed to determine the availability of weight loss programs. Services for high-risk health needs common to university campuses (ie, alcohol and other drugs, victim services, sexual health, and eating disorders) were searched. RESULTS: Of the universities searched, 3 (30%, 3/10) offered weight loss programming, however, none met the predetermined criteria. Comparatively, all schools (100%, 10/10) offered no-cost and continual enrollment programming for the other high-risk health needs. CONCLUSIONS: There are limited weight loss services available to undergraduate students compared with other university services. Collaboration between existing college health service providers is suggested for the delivery of appropriate programming for overweight and obese undergraduates wanting to lose weight.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.435
Teacher spread0.360 · 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.

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

Citations16
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Public Health and SurveillanceSame topicObesity and Health PracticesFrench-language works237,207