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Record W2023227583 · doi:10.1080/0886571x.2014.958346

Developing a Clinical Framework for Children/Youth Residential Treatment

2014· article· en· W2023227583 on OpenAlexaff
Ajit Ninan, Gillian Kriter, Margaret Steele, Linda Baker, Jim Boniferro, Jennifer Crotogino, Shannon L. Stewart, Nevena Dourova

Bibliographic record

VenueResidential Treatment for Children & Youth · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsChild and Family Research InstituteWestern University
Fundersnot available
KeywordsPsychological interventionPsychologyResidential careMedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

The idea of implementing consistent overall clinical approaches in residential treatment facilities may improve care by influencing family engagement, interactional styles of residential staff and clinicians with children and youth, specific therapeutic interventions including the management of problematic behaviors, milieu approaches, and group interventions, as well as inform programmatic structure, rules, and expectations of residential living. A clinical framework should demonstrate contemporary and evidence supported practices in order to achieve meaningful success. This article summarizes a literature search and discussion points regarding family/community engagement, individualized assessment/treatment, interprofessional teams, and organizational factors in developing a clinical framework for children/youth residential treatment.

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)0.029
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0100.020
Scholarly communication0.0100.007
Open science0.0060.008
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.377
Teacher spread0.305 · 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 designTheoretical or conceptual
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

Citations7
Published2014
Admission routes1
Has abstractyes

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