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Record W2529332086 · doi:10.1192/bjp.bp.116.184010

Mediation analysis of severity of needs, service performance and outcomes for patients with mental disorders

2016· article· en· W2529332086 on OpenAlexafffund
Paul Roux, Christine Passerieux, Marie‐Josée Fleury

Bibliographic record

VenueThe British Journal of Psychiatry · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health Research
KeywordsStructural equation modelingMediationService (business)Mental healthQuality of life (healthcare)PsychologyMedicineClinical psychologyNursingPsychiatryComputer scienceBusinessMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Needs and service performance assessment are key components in improving recovery among individuals with mental disorders. AIMS: To test the role of service performance as a mediating factor between severity of patients' needs and outcomes. METHOD: A total of 339 adults with mental disorders were interviewed. A mediation analysis between severity of needs, service performance (adequacy of help, continuity of care and recovery orientation of services) and outcomes (personal recovery and quality of life) was carried out using structural equation modelling. RESULTS: The structural equation model provided a good fit with the data. An increase in needs was associated with lower service performance and worse outcomes, whereas higher service performance was associated with better outcomes. Service performance partially mediated the effect of patient needs on outcomes. CONCLUSIONS: Poorer service performance has a negative impact on outcomes for patients with the highest needs. Ensuring more efficient services for patients with high needs may help improve their recovery and quality of life.

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)0.012
metaresearch head score (Gemma)0.050
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.326
Teacher spread0.299 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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