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Record W4205329464 · doi:10.1186/s12888-021-03668-3

Delivering collaborative mental health care within supportive housing: implementation evaluation of a community-hospital partnership

2022· article· en· W4205329464 on OpenAlexafffundabout
Lucy C. Barker, Janet Lee-Evoy, Aysha Butt, Sheila Wijayasinghe, Danielle Nakouz, Tammy Hutcheson, Kaela McCarney, Roopinder Kaloty, Simone N. Vigod

Bibliographic record

VenueBMC Psychiatry · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Social InnovationInstitute for Work & HealthWomen's College HospitalUniversity of Toronto
FundersWomen's College Hospital
KeywordsPsychoeducationMental healthMultidisciplinary approachNursingFocus groupGeneral partnershipSupportive housingMental illnessMedicinePsychologyMedical educationPsychological interventionPsychiatryBusinessPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Approaches to address unmet mental health care needs in supportive housing settings are needed. Collaborative approaches to delivering psychiatric care have robust evidence in multiple settings, however such approaches have not been adequately studied in housing settings. This study evaluates the implementation of a shifted outpatient collaborative care initiative in which a psychiatrist was added to existing housing, community mental health, and primary care supports in a women-centered supportive housing complex in Toronto, Canada. METHODS: The initiative was designed and implemented by stakeholders from an academic hospital and from community housing and mental health agencies. Program activities comprised multidisciplinary support for tenants (e.g. multidisciplinary care teams, case conferences), tenant engagement (psychoeducation sessions), and staff capacity-building (e.g. formal trainings, informal ad hoc questions). This mixed methods implementation evaluation sought to understand (1) program activity delivery including satisfaction with these activities, (2) consistency with team-based tenant-centered care and with pre-specified shared lenses (trauma-informed, culturally safe, harm reduction), and (3) facilitators and barriers to implementation over a one-year period. Quantitative data included reporting of program activity delivery (weekly and monthly), staff surveys, and tenant surveys (post-group surveys following tenant psychoeducation groups and an all-tenant survey). Qualitative data included focus groups with staff and stakeholders, program documents, and free-text survey responses. RESULTS: All three program activity domains (multidisciplinary supports, tenant engagement, staff capacity-building) were successfully implemented. Main program activities were multidisciplinary case conferences, direct psychiatric consultation, tenant psychoeducation sessions, formal staff training, and informal staff support. Psychoeducation for tenants and informal/formal staff support were particularly valued. Most activities were team-based. Of the shared lenses, trauma-informed care was the most consistently implemented. Facilitators to implementation were shared lenses, psychiatrist characteristics, shared time/space, balance between structure and flexibility, building trust, logistical support, and the embedded evaluation. Barriers were that the initial model was driven by leadership, confusion in initial processes, different workflows across organizations, and staff turnover; where possible, iterative changes were implemented to address barriers. CONCLUSIONS: This evaluation highlights the process of successfully implementing a shifted outpatient collaborative mental health care initiative in supportive housing. Further work is warranted to evaluate whether collaborative care adaptations in supportive housing settings lead to improvements in tenant- and program-level 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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.098
GPT teacher head0.488
Teacher spread0.389 · 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.

Study designQualitative
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

Citations15
Published2022
Admission routes3
Has abstractyes

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