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Record W1963507344 · doi:10.7870/cjcmh-2004-0008

Challenges Faced by Service Providers in the Delivery of Assertive Community Treatment

2004· article· en· W1963507344 on OpenAlexafffundvenueabout
Terry Krupa, Shirley Eastabrook, Peter Beattie, Richard Carrière, Dianne McIntyre, R. Woodman

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2004
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsQueen's University
FundersCanadian Mental Health AssociationOntario Ministry of Health and Long-Term Care
KeywordsAssertive community treatmentNegotiationService providerService delivery frameworkPublic relationsFraming (construction)PopulationMental healthContext (archaeology)NursingAssertivenessQualitative researchBusinessMedicineService (business)Mental illnessPsychologySociologyPolitical scienceMarketingEngineeringPsychiatryEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

This qualitative study examined the delivery of Assertive Community Treatment from the perspective of service providers of 4 ACT teams in southeastern Ontario. Overall, providers were positive about their involvement with ACT. Eight tensions experienced in the context of delivering services emerged: negotiating governance structures; providing 24-hour coverage; balancing the clinical-administrative responsibilities of team leaders; accessing hospital beds; meeting local population needs; integrating treatment and rehabilitation; changing services to meet changes in the population being served; and implementing ambiguous ACT standards. Framing these challenges in the context of ACT structures and the broader community mental health system, the study suggests possibilities for the ongoing development of the model to facilitate the realization of the ACT vision.

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)0.021
metaresearch head score (Gemma)0.042
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 score0.261
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.042
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0230.009
Scholarly communication0.0090.003
Open science0.0020.005
Research integrity0.0030.005
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 head0.106
GPT teacher head0.378
Teacher spread0.272 · 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

Citations16
Published2004
Admission routes4
Has abstractyes

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