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Factors associated with the use of intrusive measures at a tertiary care facility for children and youth with mental health and developmental disabilities

2012· article· en· W1962032054 on OpenAlexaff
Shannon L. Stewart, Philip Baiden, Laura Theall

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2012
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsChild and Family Research Institute
Fundersnot available
KeywordsMental healthPsychological interventionPsychologyLogistic regressionIntervention (counseling)HarmPsychiatryClinical psychologyMedicine

Abstract

fetched live from OpenAlex

This study seeks to identify some of the explanatory factors associated with the use of intrusive measures among children with mental health and developmental disabilities in psychiatric facilities. Intrusive intervention data were collected using an organizational database that was developed internally at a tertiary care facility. The sample was composed of 338 children/youth aged between 6 and 18 years (mean = 12.33, standard deviation = 2.70) admitted within a 2-year period. Logistic regression was used to examine the relationship between chemical restraint, physical restraint and secure isolation, and programme type after controlling for demographic and other relevant client characteristics. The study found that the number of chemical restraints and secure isolations was higher for clients with developmental disabilities than for clients with mental health, whereas the number of physical restraints was lower for clients with developmental disabilities than clients with mental health issues. Demographic variables also predicted specific types of intrusive measures. The results of this study outline the differential factors associated with specific types of intrusive measures to control aggressive and self-harm behaviours. The paper also outlines cultural change initiatives, organizational interventions, and policy implications for best practice services for children/youth in psychiatric facilities to further reduce intrusive measures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.386
Teacher spread0.286 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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