Evaluating the Impact of a Safe Exercise Training on Clinician Knowledge and Self‐Efficacy in Managing Dysfunctional Exercise in an Eating Disorder Treatment Setting
Bibliographic record
Abstract
INTRODUCTION: Dysfunctional exercise (DEX) is common among individuals with an eating disorder (ED) and poses significant challenges to treatment and recovery. While safe and nutritionally supported physical activity can enhance treatment outcomes without hindering weight restoration, clinicians often hesitate to address DEX with their patients. This mixed-method study aimed to evaluate the impact of a Safe Exercise at Every Stage (SEES) informed training on clinician knowledge and self-efficacy in managing DEX during ED treatment. METHODS: Clinicians from a national ED centre completed measures of knowledge and self-efficacy about managing DEX before (n = 96) and after (n = 44) a synchronous virtual training. A subset of participants (n = 7) participated in semi-structured interviews to gather their perceptions of exercise in the context of EDs and feedback on the training. RESULTS: Paired sample t-tests revealed a significant increase in both self-efficacy and knowledge following the SEES training. Thematic analysis identified three key themes: (1) the Current State, (2) the Training Impact and (3) bridging the Gap Through Staff and Community Support. Results are contextualized within the broader ED field, highlighting how training and policy changes can enhance clinician confidence in managing DEX. CONCLUSION: DEX continues to be preceived as a core ED symptom. To improve its managment clinician training and shifts in overarching field ideologies are needed. Providing programme-wide training is fundamental to equipping clinicians with the skills needed to address DEX and ultimately improve patient outcomes.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".