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Record W4206534267 · doi:10.1186/s13033-021-00510-x

Whose responsibility? Part 1 of 2: A scale to assess how stakeholders apportion responsibilities for addressing the needs of persons with mental health problems

2022· article· en· W4206534267 on OpenAlexafffundabout
Srividya N. Iyer, Megan A. Pope, Aarati Taksal, Greeshma Mohan, R. Thara, Heleen Loohuis, Jai Shah, Ridha Joober, Norbert Schmitz, Howard C. Margolese, Padmavati Ramachandran, Ashok Malla

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsMcGill University Health CentreMcGill UniversityDouglas Mental Health University Institute
FundersNational Institute of Mental HealthFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research Chairs
KeywordsHealth administrationMental healthScale (ratio)PsychologyPublic relationsBusinessPublic healthNursingMedicinePolitical sciencePsychiatryGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with mental health problems have multiple, often inadequately met needs. Responsibility for meeting these needs frequently falls to patients, their families/caregivers, and governments. Little is known about stakeholders' views of who should be responsible for these needs and there are no measures to assess this construct. This study's objectives were to present the newly designed Whose Responsibility Scale (WRS), which assesses how stakeholders apportion responsibility to persons with mental health problems, their families, and the government for addressing various needs of persons with mental health problems, and to report its psychometric properties. METHODS: The 22-item WRS asks respondents to assign relative responsibility to the government versus persons with mental health problems, government versus families, and families versus persons with mental health problems for seven support needs. The items were modelled on a World Values Survey item comparing the government's and people's responsibility for ensuring that everyone is provided for. We administered English, Tamil, and French versions to 57 patients, 60 family members, and 27 clinicians at two early psychosis programs in Chennai, India, and Montreal, Canada, evaluating test-retest reliability, internal consistency, and ease of use. Internal consistency estimates were also calculated for confirmatory purposes with the larger samples from the main comparative study. RESULTS: Test-retest reliability (intra-class correlation coefficients) generally ranged from excellent to fair across stakeholders (patients, families, and clinicians), settings (Montreal and Chennai), and languages (English, French, and Tamil). In the standardization and larger confirmatory samples, internal consistency estimates (Cronbach's alphas) ranged from acceptable to excellent. The WRS scored average on ease of comprehension and completion. Scores were spread across the 1-10 range, suggesting that the scale captured variations in views on how responsibility for meeting needs should be distributed. On select items, scores at one end of the scale were never endorsed, but these reflected expected views about specific needs (e.g., Chennai patients never endorsed patients as being substantially more responsible for housing needs than families). CONCLUSIONS: The WRS is a promising measure for use across geo-cultural contexts to inform mental health policies, and to foster dialogue and accountability among stakeholders about roles and responsibilities. It can help researchers study stakeholders' views about responsibilities, and how these shape and are shaped by sociocultural contexts and mental healthcare systems.

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)0.007
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.187
GPT teacher head0.396
Teacher spread0.209 · 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 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

Citations12
Published2022
Admission routes3
Has abstractyes

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