Nursing care in mental health: Human rights and ethical issues
Bibliographic record
Abstract
People with mental illness are subjected to stigma and discrimination and constantly face restrictions in the exercise of their political, civil and social rights. Considering this scenario, mental health, ethics and human rights are key approaches to advance the well-being of persons with mental illnesses. The study was conducted to review the scope of the empirical literature available to answer the research question: What evidence is available regarding human rights and ethical issues regarding nursing care to persons with mental illnesses? A scoping review methodology guided by Arksey and O'Malley was used. Studies were identified by conducting electronic searches on CINAHL, PubMed, SCOPUS and Hein databases. Of 312 citations, 26 articles matched the inclusion criteria. The central theme which emerged from the literature was "Ethics and Human Rights Boundaries to Mental Health Nursing practice". Mental health nurses play a key and valuable role in ensuring that their interventions are based on ethical and human rights principles. Mental health nurses seem to have difficulty engaging with the ethical issues in mental health, and generally are dealing with acts of paternalism and with the common justification for those acts. It is important to open a debate regarding possible solutions for this ethical dilemma, with the purpose to enable nurses to function in a way that is morally acceptable to the profession, patients and members of the public. This review may serve as an instrument for healthcare professionals, especially nurses, to reflect about how to fulfil their ethical responsibilities towards persons with mental illnesses, protecting them from discrimination and safeguarding their human rights, respecting their autonomy, and as a value, keeping the individual at the centre of ethical discourse.
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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 | 0.067 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".