Whose responsibility is it to address the needs of persons with mental health problems? Development of a scale to assess how stakeholders distribute responsibilities across geo-cultural contexts
Bibliographic record
Abstract
Abstract 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 vis-à-vis persons with mental health problems, government vis-à-vis families, and families vis-à-vis 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. Results Test-retest reliability (intra-class correlation coefficients) ranged from excellent to good across stakeholders (patients, families, and clinicians); settings (Montreal and Chennai), and languages (English, French, and Tamil). 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 are shaped by and shape sociocultural contexts and mental healthcare systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".