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Record W2155784351 · doi:10.1177/1049732311413782

Client-Centered Design of Residential Addiction and Mental Health Care Facilities

2011· article· en· W2155784351 on OpenAlexaffabout
Gabriela Novotná, Karen Urbanoski, Brian Rush

Bibliographic record

VenueQualitative Health Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMental healthFocus groupNursingAddictionPsychologyMedicineApplied psychologyBusinessPsychiatryMarketing

Abstract

fetched live from OpenAlex

In this article we discuss the findings from a series of focus groups conducted as part of a 3-year, mixed-method evaluation of clinical programs in a large mental health and substance use treatment facility in Canada. We examined the perceptions of clinical personnel on the physical design of new treatment units and the impact on service delivery and the work environment. The new physical design appeared to support client recovery and reduce stigma; however, it brought certain challenges. Participants reported a compromised ability to monitor clients, a lack of designated therapeutic spaces, and insufficient workspace for staff. They also thought that physical design positively facilitated communication and therapeutic relationships among clinicians and clients, and increased team cohesion. We suggest that, from these findings, new avenues for research on achieving the important balance between client and staff needs in health facility design can be explored.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0010.001
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 head0.939
GPT teacher head0.767
Teacher spread0.173 · 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 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

Citations40
Published2011
Admission routes2
Has abstractyes

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