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Record W4401920568 · doi:10.1192/j.eurpsy.2024.795

Reducing treatment delays of first episode psychosis through policy in Canada: a mixed methods analysis of service provider perspectives

2024· article· en· W4401920568 on OpenAlexaffabout
F. Poukhovski-Sheremetyev, Yvonne Pelling, James M. Denny, Amal Abdel‐Baki, Shankar S. Iyer, Valérie Noel

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversité de MontréalCape Breton UniversityMcGill UniversityDouglas College
Fundersnot available
KeywordsPsychosisService providerService (business)PsychiatryPsychologyBusinessMarketing

Abstract

fetched live from OpenAlex

Introduction Young people with a first episode of psychosis can achieve full remission with prompt treatment. Throughout Canada, early psychosis intervention programs are implementing policies to ensure timely delivery of services. One of Canada’s first early intervention services, the Prevention and Early Intervention for Psychosis program, set the guideline that all youth referred should receive an appointment within 72 hours. The availability of early intervention programs has increased significantly but the standards these programs have adopted to ensure timely delivery of services remains unknown. Objectives This project aims to identify the policies and practices in early intervention programs that ensure timely delivery of services. Secondly, the project aims to understand the level of awareness of the 72-hour recommendation and the level of adoption of this recommendation. Thirdly, the project aims to identify the factors that facilitate and hinder a program’s ability to reach and maintain their benchmarks for timely delivery of services. Methods Participants included 17 service delivery providers from four early intervention programs located in socio-culturally distinct regions in Canada. Participants completed a survey about their program’s service delivery policies and practices. We led individual semi-structured interviews with seven service providers to identify the barriers and facilitators to delivering timely care. We conducted frequency analyses of the survey data and thematic analysis of the interviews to identify emerging themes. Results Forty-one percent of survey respondents indicated that their program implemented formal policies to minimize the delay to the first appointment, with benchmarks ranging from 72 hours to 12 weeks. The majority of program managers interviewed were aware of the 72-hour benchmark, voiced satisfaction with standards, and felt that establishing standards was helpful to delivering quality services. Average time between referral and first appointment ranged from 10 days to 12 weeks; however, more than half of survey respondents were unaware of the average delay in their program. Notable barriers to implementation included patient non-responsiveness, insufficient staffing, and missing patient contact information from referrals. The service providers reported engaged staff, flexible schedules, and team-based care as facilitators to meeting service delivery benchmarks. Conclusions Benchmarks such as the 72-hour recommendation are an excellent step in improving timeliness of delivery of early intervention services. Common barriers to meeting benchmarks, such as patient adherence and staff resources may be difficult to overcome; however, implementing standardized referral forms and processes, increasing staff engagement, providing flexible schedules, and encouraging team-based care could improve timely delivery of services. Disclosure of Interest None Declared

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)0.047
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 score0.840
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.012
Science and technology studies0.0110.003
Scholarly communication0.0070.002
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.395
Teacher spread0.361 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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