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Record W2902823850 · doi:10.1177/1049732318812422

“Everyone Has a Role”: Perspectives of Service Users With First-Episode Psychosis, Family Caregivers, Treatment Providers, and Policymakers on Responsibility for Supporting Individuals With Mental Health Problems

2018· article· en· W2902823850 on OpenAlexafffundabout
Megan A. Pope, Gerald Jordan, Shruthi Venkataraman, Ashok Malla, Srividya N. Iyer

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersNational Institute of Mental HealthFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsMental healthService providerMental health servicePsychosisPsychologyPsychiatryService (business)Mental healthcareNursingMedicineBusiness

Abstract

fetched live from OpenAlex

Varying perceptions of who should be responsible for supporting individuals with mental health problems may contribute to their needs remaining unmet. A qualitative descriptive design was used to explore these perceptions among key stakeholders. Focus groups were conducted with 13 service users, 12 family members, and 18 treatment providers from an early psychosis intervention program in Montreal, Canada. Individual interviews were conducted with six mental health policy-/decision-makers. Participants across stakeholder groups assigned a range of responsibilities to individuals with mental health problems, stakeholders in these individuals' immediate and extended social networks (e.g., families), macro-level stakeholders with influence (e.g., government), and society as a whole. Perceived failings of the health care system and the need for greater sharing of roles and responsibilities also emerged as important themes. Our findings suggest that different stakeholders should collectively assume certain responsibilities and that systems-level failings may contribute to unmet needs for mental health support.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.515
GPT teacher head0.584
Teacher spread0.069 · 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 designQualitative
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

Citations34
Published2018
Admission routes3
Has abstractyes

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