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Therapeutic relationships: from psychiatric hospital to community

2005· article· en· W2121845530 on OpenAlexafffund
Cheryl Forchuk, Mary‐Lou Martin, Yee‐Ching Lilian Chan, Elsabeth Jensen

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2005
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsSt Joseph's Health CareYork UniversityMcMaster UniversityLawson Health Research Institute
FundersStichting Diabetes Onderzoek NederlandDonner Canadian FoundationCanadian Health Services Research FoundationLawson Health Research Institute
KeywordsMedicineDemographicsIntervention (counseling)Hospital dischargeTransitional careQuality of life (healthcare)Mental healthDischarge planningCommunity hospitalRandomized controlled trialEmergency medicineHealth careGerontologyPsychiatryNursingDemographyIntensive care medicine

Abstract

fetched live from OpenAlex

The objective of this study was to determine the cost and effectiveness of a transitional discharge model (TDM) of care with clients who have a chronic mental illness. The model was tested in a randomized clinical trial using a cluster design. This model consisted of: (1) Peer support for 1 year and (2) Ongoing support from hospital staff until a therapeutic relationship was established with the community care provider. Participants (n = 390) were interviewed at discharge, 1 month post-discharge, 6 months post-discharge and 1 year post-discharge. Data collected included demographics, quality of life, health care utilization, levels of functioning and the degree of intervention received. The intervention group post-discharge costs and quality of life were not significantly improved compared with the control group. Although not predicted a priori, intervention subjects were discharged an average of 116 days earlier per person. Based on the hospital per diem rate this would be equivalent to 12M dollars CDN hospital costs. Both under-implementation among implementation wards and contamination in control wards were found. This study demonstrates some of the multiple challenges in health system research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.376
Teacher spread0.337 · 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.

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

Citations120
Published2005
Admission routes2
Has abstractyes

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