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Record W2520207463 · doi:10.1177/1359105316666654

Physical health, community participation and schizophrenia

2016· article· en· W2520207463 on OpenAlexafffund
Pooja Patel, Tyler Frederick, Sean A. Kidd

Bibliographic record

VenueJournal of Health Psychology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre for Addiction and Mental HealthOntario Tech UniversityUniversity of Toronto
FundersOntario Mental Health FoundationMental Health Commission
KeywordsGrounded theoryMental healthPhysical healthPsychologySchizophrenia (object-oriented programming)Qualitative researchCommunity healthPsychiatryGerontologyMedicinePublic healthSociologyNursing

Abstract

fetched live from OpenAlex

Our objective is to identify links between physical health and community participation among individuals with schizophrenia or a psychosis mental illness. Semi-structured qualitative and quantitative interviews and community tours were conducted over 10 months ( N = 30). Interviews were transcribed and analyzed using a grounded theory coding strategy. Physical health played an important role in community participation both as a cause and consequence. Key processes included mobility issues impeding physical community involvement; a multi-directional relationship between social relationships, community involvement, and physical health; identity as a mechanism linking physical health problems and community engagement; and the potential for community-based mental health programs.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.386
GPT teacher head0.570
Teacher spread0.184 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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