Bibliographic record
Abstract
ABSTRACT Body-image research has focused on younger women and girls, and tended to ignore women in later life, although recent studies have called for more research into the body image of older women, particularly from a lifecourse perspective. The lifecourse perspective can address the complexity of body image by identifying personal and/or environmental factors that shape body image and the trajectories of body image across the lifecourse. Accordingly the purpose of the study reported in this paper was to explore older women's body image using a lifecourse perspective. We conducted individual interviews and follow-up focus groups with 13 women aged 60–69 years, all of them resident in a United States non-metropolitan county (its largest city having a population of 38,420) and having lived in the country for more than 30 years. The findings highlight the influence of inter-personal relationships ( e.g . with a spouse or parent), the macro-environment ( e.g . media or community attitudes) and key life events ( e.g . physiological changes or educational experiences) that shaped body image at various life stages. In addition, the findings demonstrate that as women age, they de-prioritise appearance in favour of health or internal characteristics. Finally, the findings highlight the complexity of body image as a construct, which includes attitudes toward appearance, evaluations of health and physical ability, and assessments of appearance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".