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Record W3163904039 · doi:10.1177/10497315211013908

Trauma-Informed Organizational Dynamics and Client Outcomes in Concurrent Disorder Treatment

2021· article· en· W3163904039 on OpenAlexaff
Micheal L. Shier, Aaron Turpin

Bibliographic record

VenueResearch on Social Work Practice · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntrapersonal communicationPsychologyEmpowermentConcurrent validityEmpirical researchContext (archaeology)Clinical psychologyApplied psychologySocial psychologyInterpersonal communicationPsychometrics

Abstract

fetched live from OpenAlex

Purpose: To test an empirical model of the effects of a trauma-informed organizational environment on service user outcomes in the context of concurrent disorder treatment. Methods: Service users ( n = 172) were surveyed while in treatment to determine the effects of trauma-informed organizational dynamics (i.e., safety, trust, choice, collaboration, and empowerment) on service user intrapersonal development outcomes (i.e., self-awareness, outlook, coping ability, self-worth, and self-determination) and improvements with concurrent disorder behaviors. After testing for validity and reliability of latent factors, data were analyzed using multivariate analysis. Results: As a concise analytical model, the trauma-informed organizational environment was found to significantly positively predict all service user intrapersonal outcomes as well as a reduction in concurrent disorder behaviors. Conclusions: This study informs developments in the design and implementation of trauma-informed practice frameworks for concurrent disorder treatment and emphasizes the importance of adapting organizational environments to support improved client outcomes.

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)0.011
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.123
GPT teacher head0.531
Teacher spread0.408 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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