Reduction of adverse events in a psychiatric inpatient eating disorder unit during the COVID‐19 pandemic
Bibliographic record
Abstract
TOPIC: Globally, the COVID-19 pandemic had impacted the health care delivery including inpatient psychiatric facilities. Within psychiatric settings, life of inpatients was profoundly altered. PURPOSE: This paper aimed to understand if pandemic-related changes within an inpatient Eating Disorder Unit in a specialized psychiatric hospital in Ontario, Canada impacted incidence of aggression and use of coercive methods among adolescents. SOURCE USED: An exploratory study design was used to examine incidence of aggression, self-harm, code whites, staff assist, restraints and seclusion, and nasogastric feeding (NGF) among adolescents with eating disorders before and after the modified service delivery within the inpatient unit. Descriptive analyses were conducted. RESULTS: Analyses revealed a complete reduction in episodes of self-harm, aggression, staff assists, use of restraint and seclusion as well as an 80.14% reduction on average use of NGF. CONCLUSION: Authors speculate that the change in environment and program delivery method, peer influence, and shift in power relations between patient and staff may have resulted in improved experiences. This report provides insights to adopt a recovery-oriented service delivery for adolescents with eating disorders in inpatient settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".