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Record W2464351606 · doi:10.5539/gjhs.v9n2p7

Self-Empowerment of Female Students in Prevention of Osteoporosis

2016· article· en· W2464351606 on OpenAlexvenueno aff
Nader Sharifi, Fereshteh Majlessi

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoporosisEmpowermentMedicineLife expectancySignificant differenceGerontologyPopulationSelf-efficacyDescriptive statisticsPhysical therapyPsychologyInternal medicineEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

<p><strong>BACKGROUND:</strong> Osteoporosis is a chronic disease affecting society, particularly women and girls. Osteoporosis is a chronic, multifactorial disease, which is currently prevalent as the life expectancy and aging population is increasing. The purpose of this study is to evaluate self-empowerment (knowledge, attitudes, life skills and self-efficacy) of female students for prevention of osteoporosis.</p><p><strong>METHODS:</strong> This study used a descriptive survey. Participants included 60 female students of Islamic Azad University, Sharekord. Data was collected by a researcher-made questionnaire measuring self-empowerment for prevention of osteoporosis. In addition to descriptive indicators, t-test and chi-square test were used to analyze data by SPSS software.</p><p><strong>RESULTS:</strong> Self-empowerment of female students, including attitude, social skills and self-efficacy, is optimal for prevention of osteoporosis. The mean of these three components is significantly higher than the assumed mean (3). However, their knowledge is not optimal. There is no significant difference in frequency of correct and incorrect responses.</p><p><strong>CONCLUSION:</strong> Female students do not have adequate knowledge for prevention of osteoporosis and require training in this area.</p>

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)0.004
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 score0.034
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.419
Teacher spread0.393 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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