Consensus‐based perspectives of pediatric inpatient eating disorder services
Bibliographic record
Abstract
OBJECTIVE: There are few evidence-based guidelines for inpatient pediatric eating disorders. The aim was to gain perspectives from those providing and receiving inpatient pediatric eating disorder care on the essential components treatment. METHOD: A modified Delphi technique was used to develop consensus-based opinions. Participants (N = 74) were recruited for three panels: clinicians (n = 24), carers (n = 31), and patients (n = 19), who endorsed three rounds of statements online. RESULTS: A total of 167 statements were rated, 79 were accepted and reached a consensus level of at least 75% across all panels, and 87 were rejected. All agreed that families should be involved in treatment, and thatpsychological therapy be offered in specialist inpatient units. Areas of disagreement included that patients expressed a desire for autonomy in sessions being available without carers, and that weight gain should be gradual and admissions longer, in contrast to carers and clinicians. Carers endorsed that legal frameworks should be used to retain patients if required, and that inpatients are supervised at all times, in contrast to patients and clinicians. Clinicians endorsed that food access should be restricted outside meal times, in contrast to patients and carers. DISCUSSION: The findings indicate areas of consensus in admission criteria, and that families should be involved in treatment, family involvement in treatment, while there was disagreement across groups on topics including weight goals and nutrition management. Perspectives from patients, carers, and clinicians may be useful to consider during future revisions of best practice guidelines.
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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 | 0.071 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".