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Record W2943775537 · doi:10.1177/1757975918816705

Gendered perceptions of osteoporosis: implications for youth prevention programs

2019· article· en· W2943775537 on OpenAlexaffabout
Alyson Holland, Tina Moffat

Bibliographic record

VenueGlobal Health Promotion · 2019
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOsteoporosisQualitative researchHealth belief modelMedicineGerontologyAmbivalenceDiseasePerceptionPsychologyClinical psychologyHealth promotionPublic healthNursingSocial psychologySociology

Abstract

fetched live from OpenAlex

The presentation of osteoporosis as a woman's disease in prevention information influences how osteoporosis is perceived and how prevention information is internalized and applied. Using the Health Belief Model as a framework, gendered perceptions of osteoporosis were investigated in Canadian young adults to inform the design of prevention programs. A combination of the Osteoporosis Health Belief Scale (OHBS) and semi-structured interviews were used to explore participants' perceptions of osteoporosis severity, susceptibility, and motivation to engage in prevention activities. Sixty multiethnic men and women aged 17-30 years living in Hamilton, Ontario, Canada participated in the study. While the findings from the OHBS indicated that both genders scored high for self-efficacy, the results from the qualitative interviews showed ambivalent attitudes toward prevention behaviors, indicating a disconnect between quantitative and qualitative findings. Perceptions related to severity and susceptibility revealed that while osteoporosis was generally viewed as a woman's disease, perceived individual risk of disease was a negotiation between larger gender constructs of osteoporosis and a variety of risk factors. This study indicates that osteoporosis prevention programs should consider actively acknowledging gendered and youth-based conceptions of osteoporosis in order to increase prevention behaviors in the whole population to reduce future disease.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.146
GPT teacher head0.507
Teacher spread0.361 · 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.

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

Citations9
Published2019
Admission routes2
Has abstractyes

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