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Record W4381716201 · doi:10.1111/jcap.12430

Reduction of adverse events in a psychiatric inpatient eating disorder unit during the COVID‐19 pandemic

2023· article· en· W4381716201 on OpenAlexaffabout
Simone Arbour, Sayani Paul, Mark J. Rice

Bibliographic record

VenueJournal of Child and Adolescent Psychiatric Nursing · 2023
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsSeclusionAggressionPsychiatryMedicinePandemicPsychiatric hospitalInpatient careIncidence (geometry)Unit (ring theory)Clinical psychologyPsychologyHealth careCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.320
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Child and Adolescent Psychiatric NursingSame topicEating Disorders and BehaviorsFrench-language works237,207