Promoting regular physical activity for postemenopausal women
Bibliographic record
Abstract
Postmenopausal women are at heightened risks for developing metabolic diseases (Dubnov, Brzezinski & Berry, 2003). Although regular physical activity (PA) can be a viable buffer against the onset of metabolic diseases, the best strategies available to increase PA rates amongst postmenopausal women still remain unclear (Teoman, Öczan & Acar, 2004). Therefore the purpose of this study was to determine what physical and environmental: barriers, strategies and outcomes affect motives to increase PA rates among postmenopausal women. Semi-structured interviews were conducted with 11 postmenopausal women who had completed a 12-week structured exercise program, to understand the factors that influenced their motives for PA. Double coding and inductive content analyses of interview transcripts yielded emergent themes stemming from Social Cognitive Theory constructs. Though weather related barriers significantly deterred PA participation, self-regulatory strategies such as using logsheets, self-talk and at-home exercise equipment enhanced participants' motivation to overcome such barriers and remain active. Enhanced levels of self-efficacy emerged as the women explained how they had coped with barriers and realized effective solutions tailored to their unique preferences and contexts. These main findings suggest that tailoring interventions to the environmental and motivational needs of participants can influence health outcomes of at-risk populations, specifically postmenopausal women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".