Is a Picture Worth a Thousand Words? Using Photo-Elicitation to Study Body Image in Middle-to-Older Age Women With and Without Multiple Sclerosis
Bibliographic record
Abstract
In this study, we explored how women with varying relationships to disability and aging used photographs to represent their body image experiences. Seven middle-aged and older adult women with and without multiple sclerosis were asked to provide up to 10 photographs that represented their body image and complete a one-on-one interview. We used reflexive thematic analysis to develop themes and interpret the findings. Overall, the women expressed not only complicated relationships with their bodies, represented through symbolism, scrutiny of body features (e.g., posture, varicose veins, and arthritis) but also deep reflection linked to positive body image and resilience. These findings revealed not only the nuanced experiences women have with aging, disability, and gender but also the commonly experienced ingrained views of body appearance as each participant illustrated a difficult negotiation with the aesthetic dimension of their body image. Finally, we provide important implications of the use of visual methods in body image research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".