MétaCan
Menu
Back to cohort
Record W2179062058 · doi:10.7870/cjcmh-2014-019

Assertive Community Treatment (ACT) in a Rural Canadian Community: Client Characteristics, Client Satisfaction, and Service Effectiveness

2014· article· en· W2179062058 on OpenAlexaffvenueabout
Leslie M. Pope, Gregory E. Harris

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2014
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAssertive community treatmentAuditSchizoaffective disorderMedicineAssertivenessPsychiatryCustomer satisfactionEmergency departmentFamily medicinePsychologyNursingMental healthPsychosisBusiness

Abstract

fetched live from OpenAlex

This study describes an assertive community treatment (ACT) model in a rural Canadian location and examines characteristics of ACT service users, their degree of satisfaction with ACT, and whether their engagement with ACT resulted in reduced reliance on acute psychiatric services and hospital emergency room use. Chart audits were used to collect demographic and clinical participant data, including days of psychiatric admission and emergency room (ER) visits. Twenty-nine ACT clients agreed to participate. The majority of participants (82.8%) were male and had been diagnosed with schizophrenia or a schizoaffective disorder (65.5%). There was a high rate of concurrent substance abuse (75.9%). The average number of readmission days was reduced from 14 to 0 (p < 0.05) following engagement with ACT, and the average number of visits to ER s was reduced from 3 to 1 (p < 0.05). Participants reported overall high satisfaction with ACT services. Study implications for policy and practice are discussed along with future research recommendations.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.330
Teacher spread0.294 · 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 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

Citations4
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Community Mental HealthSame topicSchizophrenia research and treatmentFrench-language works237,207