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Record W2614027868 · doi:10.1186/s13033-017-0137-3

What are effective strategies for implementing trauma-informed care in youth inpatient psychiatric and residential treatment settings? A realist systematic review

2017· review· en· W2614027868 on OpenAlexafffund
Stephanie A. Bryson, Emma Gauvin, Ally Jamieson, Melanie R Rathgeber, Lorelei Faulkner-Gibson, Sarah Bell, Jana Davidson, Jennifer Russel, Sharlynne Burke

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2017
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsB.C. Women's Hospital & Health CentreBC Children's HospitalUniversity of British Columbia
FundersUniversity of British ColumbiaBC Children's Hospital
KeywordsSeclusionMental healthPsychological interventionPsychiatryChild and adolescent psychiatryMedicineIntervention (counseling)PsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Many young people who receive psychiatric care in inpatient or residential settings in North America have experienced various forms of emotional trauma. Moreover, these settings can exacerbate trauma sequelae. Common practices, such as seclusion and restraint, put young people at risk of retraumatization, development of comorbid psychopathology, injury, and even death. In response, psychiatric and residential facilities have embraced trauma-informed care (TIC), an organizational change strategy which aligns service delivery with treatment principles and discrete interventions designed to reduce rates of retraumatization through responsive and non-coercive staff-client interactions. After more than two decades, a number of TIC frameworks and approaches have shown favorable results. Largely unexamined, however, are the features that lead to successful implementation of TIC, especially in child and adolescent inpatient psychiatric and residential settings. METHODS: Using methods proposed by Pawson et al. (J Health Serv Res Policy 10:21-34, 2005), we conducted a modified five-stage realist systematic review of peer-reviewed TIC literature. We rigorously searched ten electronic databases for peer reviewed publications appearing between 2000 and 2015 linking terms "trauma-informed" and "child*" or "youth," plus "inpatient" or "residential" plus "psych*" or "mental." After screening 693 unique abstracts, we selected 13 articles which described TIC interventions in youth psychiatric or residential settings. We designed a theoretically-based evaluative framework using the active implementation cycles of the National Implementation Research Network (NIRN) to discern which foci were associated with effective TIC implementation. Excluded were statewide mental health initiatives and TIC implementations in outpatient mental health, child welfare, and education settings. Interventions examined included: Attachment, Self-Regulation, and Competency Framework; Six Core Strategies; Collaborative Problem Solving; Sanctuary Model; Risking Connection; and the Fairy Tale Model. RESULTS: Five factors were instrumental in implementing trauma informed care across a spectrum of initiatives: senior leadership commitment, sufficient staff support, amplifying the voices of patients and families, aligning policy and programming with trauma informed principles, and using data to help motivate change. CONCLUSIONS: Reduction or elimination of coercive measures may be achieved by explicitly targeting specific coercive measures or by implementing broader therapeutic models. Additional research is needed to evaluate the efficacy of both approaches.

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)0.049
metaresearch head score (Gemma)0.153
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.153
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.011
Bibliometrics0.0180.011
Science and technology studies0.0010.002
Scholarly communication0.0060.008
Open science0.0040.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.088
GPT teacher head0.465
Teacher spread0.377 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations186
Published2017
Admission routes2
Has abstractyes

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