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Record W1965104571 · doi:10.1108/17466660200800015

Measuring outcomes in a child psychiatry inpatient unit

2008· article· en· W1965104571 on OpenAlexaff
M. Elena Garralda, Gillian Rose, Ruth P. Dawson

Bibliographic record

VenueJournal of Children s Services · 2008
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMedicinePsychiatryMedical diagnosisUnit (ring theory)Child and adolescent psychiatrySchizophrenia (object-oriented programming)Clinical psychologyPsychology

Abstract

fetched live from OpenAlex

The aim of this article is to examine clinical outcomes in a child psychiatry inpatient unit using dedicated measures. Clinicians completed contextual (Paddington Complexity Scale - PCS) and clinical change (Health of the Nation Outcome Scales for Children and Adolescents - HoNOSCA) questionnaires on admission and discharge for consecutive admissions to the unit between 1999 and 2007 (n=167). Mean changes in HoNOSCA scores were analysed, and the predictors of HoNOSCA mean change were assessed using regression analysis. The results showed that the mean length of stay at the unit was 5.6 months (SD 3.1). PCS ratings identified high total, clinical, and environmental complexity scores. HoNOSCA ratings indicated high levels of psychological problems on admission and significant improvement at discharge (mean change 7.7 (SD 6.7)). Greater positive change was associated with higher initial HoNOSCA scores, diagnoses other than conduct disorder and schizophrenia, and a facilitative parental attitude. The authors conclude that the systematic use of standardised outcome measures in child psychiatric inpatient units is useful to document clinical features, complexity and clinical change.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.260
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2008
Admission routes1
Has abstractyes

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