An Evaluation of a Body Image Training Course for Health Professionals
Bibliographic record
Abstract
Health professionals (HP), including fitness trainers, are individuals who support and care for clients/patients. Despite body image being a crucial aspect of their practice, many HPs indicate insufficient preparedness to tackle body image concerns. Following a mixed-methods design, this study evaluated changes in HPs’ knowledge, skills, practical application, and their own body image and related attitudes following an 8-module body image training course. The analytic sample consisted of 47 HPs, the majority of whom identified as registered dietitians (25.0%), health coaches (19.2%), fitness trainers (21.2%), and nutritionists (13.5%). Participants completed 72.1% (SD = 33.1) of the modules and reported high satisfaction, usefulness, and understandability ratings (i.e., >92/100 %). There were statistically significant (p < .05) reductions in HPs’ self-oriented perfectionism (t(49) = 2.94, d = .42) and idealization of thin (t(49) = 4.68, d = 0.66) and athletic (t(49) = 5.13, d = .73) body ideals. HPs also exhibited increased body appreciation (t(49) = -2.98, d = -.42) and higher scores on a researcher-devised body image knowledge quiz (t(44) = -5.89, d = -.88). Thematic analyses were conducted on open-ended responses regarding implementation of course content, acquired skills, and feedback for improvement. HPs noted a shift towards individualized, compassionate practices and an increased readiness to address body image. The course's impact on their own body image and its potential as an ongoing educational resource was also highlighted. These findings underscore the feasibility and transformative potential of a comprehensive body image training course for HPs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".