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School Re-Entry of Adolescent Patients Discharged fromPsychiatric Hospital: One Step in Continuous QualityImprovement

2021· article· en· W3213026411 on OpenAlexafffund
Michèle Preyde, Shrenik Parekh, Anna Markov, Hayley Carpenter, John Heintzman

Bibliographic record

VenueAdolescent Psychiatry · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsGrand River HospitalUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychological resilienceMedicinePsychiatryPsychologyClinical psychology

Abstract

fetched live from OpenAlex

Objective: School re-entry following hospitalization for psychiatric care has been reported as difficult for many adolescent patients. Continuous quality improvement initiatives may improve programming to enhance school re-entry experiences. The purpose for this study was to explore the school re-entry perspectives of the youth discharged from a psychiatric inpatient unit after implementing programs that patients previously identified as needed. Methods: A survey was administered to the youth about one month after discharge to gather their perspective of their school re-entry, along with self-rated resilience and stress. Results: Twenty-six youth (23%) participated in the post-discharge survey who reported a mean age of 15.6 years (SD 1.0), 77% identified as female, 13 (50%) provided very positive re-entry comments, eight (31%) reported moderately positive experiences, and five (19%) reported a very poor school re-entry. Mean perceived resilience (4.01, SD 0.6) and stress (3.42, SD 0.8) scores suggest youth thought they had good resilience and moderate stress. Conclusions: Most youth reported a good school re-entry. Considerable concerns remain for the 19% who reported a poor school re-entry who may benefit from specialized outpatient or day programming post-discharge before attempting a return to school. Future directions for research are provided.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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