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Record W1969148426 · doi:10.5993/ajhb.35.3.10

Predicting Osteoporosis Prevention Behaviors: Health Beliefs and Knowledge

2011· article· en· W1969148426 on OpenAlexaff
Kimberley L. Gammage, Panagiota Klentrou

Bibliographic record

VenueAmerican Journal of Health Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsBrock University
Fundersnot available
KeywordsHealth belief modelOsteoporosisPsychological interventionSelf-efficacyPhysical activityPsychologyHealth behaviorCalciumDevelopmental psychologyMedicineClinical psychologyHealth promotionEnvironmental healthPhysical therapyPublic healthSocial psychologyPsychiatryEndocrinologyInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate whether the expanded Health Belief Model (EHBM) could predict calcium intake and physical activity in adolescent girls. METHODS: Participants self-reported calcium intake, physical activity, and osteoporosis health beliefs. Regression analysis examined the relationship between these beliefs and behaviors. RESULTS: Calcium self-efficacy, calcium barriers, and osteoporosis knowledge predicted calcium intake, whereas exercise self-efficacy and health motivation predicted physical activity. CONCLUSIONS: The EHBM appears to be useful in predicting osteoporosis prevention behaviors in adolescent girls. Interventions should focus on identifying barriers to calcium consumption and physical activity and increasing beliefs in the ability overcome them.

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)0.006
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.097
GPT teacher head0.435
Teacher spread0.338 · 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

Citations41
Published2011
Admission routes1
Has abstractyes

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