Research ethics in global mental health: Advancing culturally responsive mental health research
Bibliographic record
Abstract
Global mental health research is needed to inform effective and efficient services and policy interventions within and between countries. Ethical reflection should accompany all GMHR and human resource capacity endeavors to ensure high standards of respect for participants and communities and to raise public debate leading to changes in policies and regulations. The views and circumstances of ethno-cultural and disadvantaged communities in the Majority and Minority world need to be considered to enhance scientific merit, public awareness, and social justice. The same applies to people with vulnerabilities yet who are simultaneously capable, such as children and youth. The ethical principles of respect for persons or autonomy, beneficence/non-maleficence, justice, and relationality require careful contextualization for research involving human beings. Building on the work of Fisher and colleagues (2002), this article highlights some strategies to stimulate the ethical conduct of global mental health research and to guide decision-making for culturally responsible research, such as developing culturally sensitive informed consent and disclosure policies and procedures; paying special attention to socioeconomic, cultural, and environmental risks and benefits; and ensuring meaningful community and individual participation. Research and capacity-building partnerships, political will, and access to resources are needed to stimulate global mental health research and consolidate ethical practice.
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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 | 0.426 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.014 | 0.113 |
| Scholarly communication | 0.037 | 0.038 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.020 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".