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Record W4281675928 · doi:10.1080/07448481.2022.2076101

College students’ attitudes about ways family, friends, significant others and media affect their eating and exercise behaviors and weight perceptions

2022· article· en· W4281675928 on OpenAlexaff
Laura Nabors, K. Fiser-Gregory, Afolakemi Olaniyan, Tina L. Stanton‐Chapman, Ashley L. Merianos

Bibliographic record

VenueJournal of American College Health · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsPsychologyPsychological interventionPerceptionAffect (linguistics)College healthSocial psychologyObesityDevelopmental psychologyClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Objective: This study examined college students’ perceptions of how parents, family, friends, significant others, and the media influenced eating and exercise behaviors and weight perceptions. Participants: Forty-one college students, mostly female, participated in interviews. Methods: A Grounded Theory approach, using open coding and memoing, was used to uncover key themes. Results: Healthy cooking and exercise role models at home were viewed as positive, encouraging healthy eating and exercise. Criticism was perceived as negative for healthy habits and weight perceptions. Friends and significant others who practiced positive health habits and were body accepting were uplifting. Cultural transmission of the thin ideal could occur through the media. Some noted that media messages were becoming more positive. Conclusions: Using peers, especially friends, as collaborators in interventions, and discussing parental influences on eating, exercise, and weight perceptions may positively impact obesity prevention programs and interventions for college students.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.335
Teacher spread0.311 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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