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Record W4366384241 · doi:10.3138/cjpe.21.006

Development of a Framework for Comprehensive Evaluation of Client Outcomes in Community Mental Health Services

2006· article· en· W4366384241 on OpenAlexaffvenueabout
Joan Bishop, Evelyn Vingilis

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsWestern UniversityRiverview HospitalUniversity of British Columbia
Fundersnot available
KeywordsOperationalizationAssertive community treatmentMental healthConceptualizationStakeholderPsychologyInclusion (mineral)Mental illnessApplied psychologyProcess managementPsychiatryPublic relationsSocial psychologyBusinessComputer science

Abstract

fetched live from OpenAlex

Abstract: The conduct of outcomes research on clients with serious mental illness using community mental health services is a challenge. Causal models with inclusion of mediating and moderating variables from social sciences evaluation methods provide a framework for conceptualizing and evaluating the complexity of community mental health services. This article presents the conceptualization and development of a framework for comprehensive evaluation of client outcomes in community mental health services and describes a case example of operationalizing and testing the framework in an evaluation of Assertive Community Treatment (ACT) in Southwestern Ontario, Canada. The initial framework was developed by hypothesizing a cause-effect pathway and links among delivered treatment variables, the implementation system, external factors, and intermediate and longer term outcomes. The framework was further validated and modified through stakeholder input. All variables identified in the framework were then operationally defined and instruments with good psychometric properties were chosen to measure the variables. This framework can provide a generic example for the conduct of community mental health evaluations.

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)0.031
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.462
GPT teacher head0.577
Teacher spread0.115 · 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 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
Published2006
Admission routes3
Has abstractyes

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