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Record W1971698920 · doi:10.1186/1471-2458-10-465

Assessing weight perception accuracy to promote weight loss among U.S. female adolescents: A secondary analysis

2010· article· en· W1971698920 on OpenAlexaff
Jennifer Yost, Barbara Krainovich‐Miller, Wendy C. Budin, Robert G. Norman

Bibliographic record

VenueBMC Public Health · 2010
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of North Carolina at Chapel Hill
KeywordsOverweightMedicineBody mass indexBiostatisticsWeight lossObesityPsychological interventionDemographyLogistic regressionGerontologyDescriptive statisticsEpidemiologyStatisticsPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Overweight and obesity have become a global epidemic. The prevalence of overweight and obesity among U.S. adolescents has almost tripled in the last 30 years. Results from recent systematic reviews demonstrate that no single, particular intervention or strategy successfully assists overweight or obese adolescents in losing weight. An understanding of factors that influence healthy weight-loss behaviors among overweight and obese female adolescents promotes effective, multi-component weight-loss interventions. There is limited evidence demonstrating associations between demographic variables, body-mass index, and weight perception among female adolescents trying to lose weight. There is also a lack of previous studies examining the association of the accuracy of female adolescents' weight perception with their efforts to lose weight. This study, therefore, examined the associations of body-mass index, weight perception, and weight-perception accuracy with trying to lose weight and engaging in exercise as a weight-loss method among a representative sample of U.S. female adolescents. METHODS: A nonexperimental, descriptive, comparative secondary analysis design was conducted using data from Wave II (1996) of the National Longitudinal Study of Adolescent Health (Add Health). Data representative of U.S. female adolescents (N = 2216) were analyzed using STATA statistical software. Descriptive statistics and survey weight logistic regression were performed to determine if demographic and independent (body-mass index, weight perception, and weight perception accuracy) variables were associated with trying to lose weight and engaging in exercise as a weight-loss method. RESULTS: Age, Black or African American race, body-mass index, weight perception, and weight perceptions accuracy were consistently associated with the likeliness of trying to lose weight among U.S. female adolescents. Age, body-mass index, weight perception, and weight-perception accuracy were positively associated (p < 0.05) with trying to lose weight. Black/African American subjects were significantly less likely than their White counterparts to be trying to lose weight. There was no association between demographic or independent variables and engaging in exercise as a weight-loss method. CONCLUSIONS: Findings suggest that factors influencing weight-loss efforts, including age, race, body-mass index, weight perception, and weight-perception accuracy, should be incorporated into existing or new multi-component weight-loss interventions for U.S. adolescent females in order to help reduce the national epidemic of overweight and obesity among U.S. female adolescents.

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)0.010
metaresearch head score (Gemma)0.019
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.041
GPT teacher head0.378
Teacher spread0.337 · 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

Citations53
Published2010
Admission routes1
Has abstractyes

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