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Record W4387219893 · doi:10.1177/11786329231200707

Tracking Needs-Based Community and Specialized Services of Young Adults and Their Parents Before and During a First Episode of Psychosis (FEP): Highlighting Intervention Trajectories in FEP

2023· article· en· W4387219893 on OpenAlexafffundabout
Marie‐Hélène Morin, Maryse Proulx

Bibliographic record

VenueHealth Services Insights · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité du Québec à Rimouski
FundersFonds de Recherche du Québec-Société et CultureUniversité du Québec à Rimouski
KeywordsPsychosisIntervention (counseling)Tracking (education)PsychologyPsychiatryDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Aim: This article aims to document 10 service trajectories of young adults (YA) and their parents, informed by healthcare professionals (HP), before and during a first episode of psychosis (FEP). Design: Based on a crisis model perspective of the Life Course Theory (Elder; Elder and Shanahan) developed by Carpentier and White, and adapted to the current context to track community and specialized services trajectories. Thematic analysis was used to code responses to open-ended questions around the need for help and accessing services by young adults affected by a psychotic disorder, and their parents. Setting: In collaboration with FEP clinics, setting of choice by YA and their parents. Participants: 5 YA, 12 parents, and 6 HP. Results: 10 individual service trajectories grouped into 3 distinct types of trajectories (optimal, typical, and complex) based on grouping 5 similar characteristics, highlighting service access complexity and early intervention in FEP. Conclusion: This study is the first of its kind to examine the experiences of those who seek needs-based community and specialized services leading up to and during a FEP. Findings provide key insights related to early intervention in FEP and recommendations on improving access to such services in Quebec.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.367
Teacher spread0.301 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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