Body image and body work among older women: a review
Bibliographic record
Abstract
Purpose – The purpose of this paper is to review the literature on body image and aging among older women. Using existing qualitative research, this paper explores how aging affects body image and how women respond to body image issues as they age. Design/methodology/approach – Multiple databases were used to locate original and review articles on the topics of body image and aging, with a target population of women ages 60 years and older. The findings of the literature search were compiled, summarized and sorted to create themes. Findings – Women struggle with body image issues throughout their lives. Women tend to perceive age-related changes in appearance negatively, as a threat to their identity and social value. This is due, in part, to the sociocultural environment, which pressures women to “fight” aging and maintain an ideal (young and thin) image at all costs. Some women do come to terms with their aging body and report increased self-acceptance with age. However, others turn to various forms of body work (e.g. dieting, hair dye, makeup) in order to maintain their value in an appearance-based society. Practical implications – Poor body image can affect older women's emotional, psychological and physical health and overall well-being. Health care professionals, community workers and policy makers need to be made aware of these issues so that they can respond appropriately. Originality/value – There has been limited research exploring body image among older women. This paper identifies gaps in the literature and suggests avenues for future research in this area.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".