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Record W2938779342 · doi:10.1017/ipm.2019.10

Forging successful partnerships in psychosis research: lessons from the Cavan–Monaghan First Episode Psychosis Study

2019· article· en· W2938779342 on OpenAlexaff
Vincent Russell, Nnamdi Nkire, T. Kingston, John L. Waddington

Bibliographic record

VenueIrish Journal of Psychological Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsGeneral partnershipEarly psychosisMental healthPsychosisService (business)Intervention (counseling)Medical educationIrishPsychologyPsychiatryNursingMedicinePolitical scienceBusiness

Abstract

fetched live from OpenAlex

Embedding psychosis research within community mental services is highly desirable from several perspectives but can be difficult to establish and sustain, especially when the clinical service has a rural location at a distance from academic settings with established research expertise. In this article, we share the experience of a successful partnership in psychosis research between a rural Irish mental health service and the academic department of a Dublin medical school that has lasted over 30 years. We describe the origins and evolution of this relationship, the benefits that accrued and the challenges encountered, from the overlapping perspectives of the academic department, the mental health service and psychiatric training. We discuss the potential learning that arose from the initiative, particularly for national programme planning for early intervention in psychosis, and we explore the opportunities for enhanced training, career development and professional reward that can emerge from this type of partnership.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.741
GPT teacher head0.605
Teacher spread0.136 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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