Considerations for supporting meaningful stakeholder engagement in global mental health research
Bibliographic record
Abstract
The need to ensure that research evidence is adopted by health systems and is informed by lived experience expertise has been increasingly recognised in mental health research. In the field of global mental health (GMH), though some progress has been made, the meaningful engagement of key stakeholders in research remains low. This editorial outlines recommendations to support the meaningful engagement of policy makers and people with lived or living experience of mental illness in GMH research. Recommendations include: increasing funding structures that are designed to support meaningful engagement; urging institutions to consider administrative structures that support engagement with lower resourced partners; promoting capacity development opportunities and resources to support researchers to promote meaningful engagement; developing research governance structures that include key stakeholders; and, taking steps to ensure the needs of diverse stakeholders are met through their engagement in research. Examples of good practice from these areas are provided. Though not an exhaustive list of recommendations, this editorial represents a call to the GMH research community to take a deliberate and proactive approach to prioritising meaningful stakeholder engagement in GMH research with the ultimate goal of improving accessible and appropriate mental health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.487 | 0.603 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.049 | 0.061 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.057 | 0.064 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".