Self-Report Scales to Measure Expectations and Appearance-Related Psychosocial Distress in Patients Seeking Cosmetic Treatments
Bibliographic record
Abstract
BACKGROUND: The use of screening scales in cosmetic practices may help to identify patients who require education to modify inappropriate expectations and/or psychological support. OBJECTIVES: To describe the development and validation of scales that measure expectations (about how one's appearance and quality of life might change with cosmetic treatments) and appearance-related psychosocial distress. METHODS: The scales were field-tested in patients 18 years and older seeking facial aesthetic or body contouring treatments. Recruitment took place in clinics in the United States, United Kingdom, and Canada between February 2010 and January 2015. Rasch Measurement Theory (RMT) analysis was used for psychometric evaluation. Scale scores range from 0 to 100; higher scores indicate more inappropriate expectations and higher psychosocial distress. RESULTS: Facial aesthetic (n = 279) and body contouring (n = 90) patients participated (97% response). In the RMT analysis, all items had ordered thresholds and acceptable item fit. Person Separation Index and Cronbach alpha values were 0.88 and 0.92 for the Expectation scale, and 0.81 and 0.89 for the Psychosocial Distress scale respectively. Higher expectation correlated with higher psychosocial distress (R = 0.40, P < .001). In the facial aesthetic group, lower scores on the FACE-Q Satisfaction with Appearance scale correlated with higher expectations (R = -0.27, P = .001) and psychosocial distress (R = -0.52, P < .001). In the body contouring group, lower scores on the BODY-Q Satisfaction with Body scale correlated with higher psychosocial distress (R = -0.31, P = .003). Type of treatment and marital status were associated with scale scores in multivariate models. CONCLUSIONS: Future research could examine convergent and predictive validity. As research data are accumulated, norms and interpretation guidelines will be established. LEVEL OF EVIDENCE: 2 Risk.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".