Human rights of the mentally ill in <scp>I</scp>ndonesia
Bibliographic record
Abstract
BACKGROUND: The mentally ill are vulnerable to human rights violations, particularly in Indonesia, where shackling is widespread. AIM: The aim of this study was to understand the provision of mental health care in Indonesia, thereby identifying ways to improve care and better support carers. METHODS: Grounded theory methods were used. Study participants included health professionals, non-health professionals and individuals living with a mental disorder who were well at the time (n = 49). Data were collected through interviews conducted in 2011 and 2012. RESULTS: The core category of this grounded theory is 'connecting care' a term coined by the authors to describe a model of care that involves health professionals and non-health professionals, such as family members. Four main factors influence care-providers' decision-making: competence, willingness, available resources and compliance with institutional policy. Health professionals are influenced most strongly by institutional policy when deciding whether to accept or shift responsibility to provide care. Non-health professionals base their decisions largely on personal circumstances. Jointly-made decisions can be matched or unmatched. Unmatched decisions can result in forced provision of care, increasing risks of human rights violations. LIMITATIONS: Generalization of this grounded theory is difficult as the research was conducted in two provinces of Indonesia. CONCLUSION: Institutional policy was important in the process of connecting care for the mentally ill in Indonesia and needs to be underpinned by legislation to protect human rights. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Strengthening mental health legislation in Indonesia will allow nurses to connect care more effectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".