"My Heart Couldn't Take It": Older Women's Beliefs About Exercise Benefits and Risks
Bibliographic record
Abstract
Daily physical activity is advocated by various federal health agencies for reducing many of the health risks affecting old age, but older women are generally not heeding the message. The Health Belief Model proposes that sedentary living occurs when people believe that the risks of exercising exceed the benefits. To clarify the beliefs that act as incentives and barriers to more active living, the author asked 143 independent-living women aged 70 and older to respond to open-ended questions on their beliefs about benefits and risks for 6 fitness activities: brisk walking, aquacize, riding a bike or cycling, stretching slowly to touch the toes, modified push-ups from a kneeling position, and supine curl-ups. Content analysis organized perceived risks into 19 categories and perceived benefits into 6 categories providing original data on the conceptions that older women hold about the utility of various types of physical activity. Respondents generally recognized broad health benefits to fitness activities, but beliefs about risks were strong, anatomically specific, and sometimes sensational in description. The findings suggest that many older women feel physically vulnerable, are unsure about their actual risks and benefits in exercise settings, and, in the face of that uncertainty, report medical reasons why they should be excused from fitness-promoting exercise.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".