Who should be responsible for supporting individuals with mental health problems? A critical literature review
Bibliographic record
Abstract
BACKGROUND: Individuals with mental health problems have many support needs that are often inadequately met; however, perceptions of who should be responsible for meeting these needs have been largely unexplored. Varying perceptions may influence whether, how, and to what extent relevant stakeholders support individuals with mental health problems. AIMS: To critically evaluate the literature to determine who different stakeholders believe should be responsible for supporting individuals with mental health problems, what factors shape these perceptions, and how they relate to one another. METHOD: A critical literature review was undertaken. Following an extensive literature search, the conceptual contributions of relevant works were critically evaluated. A concept map was created to build a conceptual framework of the topic. RESULTS: Views of individual versus societal responsibility for need provision and health; the morality of caring; and attributions of responsibility for mental illness offered valuable understandings of the review questions. Creating a concept map revealed that various interrelated factors may influence perceptions of responsibility. CONCLUSIONS: Varying perceptions of who should be responsible for supporting individuals with mental health problems may contribute to unmet support needs among this group. Our critical review helps build a much-needed conceptual framework of factors influencing perceptions of responsibility. Such a framework is essential as these views iteratively shape and reflect the complex divisions of mental healthcare roles and responsibilities. Understanding these perceptions can help define relevant stakeholders' roles more clearly, which can improve mental health services and strengthen stakeholder accountability.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".