Perfectionism and disordered eating in exercise and nutrition professionals: The role of self-compassion
Bibliographic record
Abstract
Disordered eating (DE) is more prevalent among exercise and nutrition professionals (ENPs) which may be partially due to heightened levels of perfectionism. Self-actualizing strategies such as self-compassion may offer protection against DE but are not well-understood among health and wellness professionals. This cross-sectional study investigated the associations between multidimensional perfectionism (self-oriented, socially prescribed, and other-oriented perfectionism) and a two-factor model of DE: (i) weight and shape concerns and (ii) food preoccupation, among ENPs (N = 93; mean age = 33.5; 88.2 % women). Self-compassion was tested as a potential moderating factor. All three dimensions of perfectionism were positively associated with both DE factors. Self-compassion significantly moderated the relationship between self-oriented perfectionism and weight and shape concerns (b = -0.51, SE = 0.22, p = .023). Similarly, self-compassion moderated the relationship between socially prescribed perfectionism and food preoccupation (b = -0.39, SE = 0.19, p = .05). Findings suggest that self-compassion may help reduce the impact of perfectionism on DE risk in ENPs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".