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Record W4407884415 · doi:10.12927/hcpol.2024.27421

Are Small Teams a Viable Strategy to Deliver Early Psychosis Intervention Services in Rural Areas? An Ontario Fidelity Study

2024· article· en· W4407884415 on OpenAlexaffvenueabout
Avra Selick, Gordon Langill, Sandy Brooks, Janet Durbin

Bibliographic record

VenueHealthcare policy · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsFidelityIntervention (counseling)Early psychosisPsychosisRural areaBusinessMedicinePsychologyProcess managementNursingPsychiatryComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Early psychosis intervention (EPI) is a complex model of care designed to be delivered by a large multidisciplinary team. However, in practice, it is often delivered by very small teams, particularly in rural areas. This study analyzed fidelity data from over half of Ontario EPI programs (n = 24) to compare model fidelity in programs with smaller (≤2.1 staff) and larger (≥4.3 staff) teams. Few differences were identified, suggesting that small teams may be a viable option to deliver the EPI model, although both large and small teams were challenged to deliver almost a third of the elements of care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.197
GPT teacher head0.479
Teacher spread0.282 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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