Perceived confidence, competence and training in evidence-based treatments for eating disorders: a survey of clinicians in an Australian regional health service
Bibliographic record
Abstract
OBJECTIVES: Eating disorders (EDs) are challenging to treat and contribute to considerable morbidity and mortality. This study sought to identify the educational preparedness, competence and confidence of clinicians to work with people with EDs; and to identify how services might be improved. METHODS: Clinicians who worked in the emergency department, medical, paediatric wards and mental health services were invited to complete an online survey. RESULTS: From the 136 surveys returned, 73% of respondents reported little or no confidence working with EDs. There was a strong linear correlation between perceived confidence and competence and hours of education. Those with 70 or more hours of self-reported training were 2.7 times more likely to rate themselves as both confident and competent. Improving services for people with eating disorders included the provision of appropriate training, improving access to services including psychotherapy, and facilitating consistency in and continuity of care. CONCLUSIONS: To increase the confidence and competence of the workforce, regular training around EDs should be undertaken. The establishment of a specialist team to provide services across the continuum of care for people with severe or complex EDs appears warranted in a regional health service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".