The elephant in the room: Family engagement in mental health and substance use research
Bibliographic record
Abstract
There is a growing emphasis in academic research on engaging people with lived experience (PWLE) of mental health and/or substance use challenges in research projects.1 People and communities with lived experience can be included in all aspects of research processes, which is increasingly encouraged by funding bodies and institutions.While engagement has grown rapidly in recent years, 2 the movement is built upon decades of progressive experience in decentring academic work across disciplines through key informant collaboration.Lived experience engagement in research, also known as 'patient engagement' or 'patient and public involvement', provides many benefits to the research process, as PWLE are subject-matter experts and key stakeholders.PWLE engagement occurs across the health disciplines, in a wide variety of research designs, including an extensive body of mental health and substance use research.2 Engaging PWLE promotes the inclusion of perspectives that matter, and stimulates the healing of the injustices of past and current imbalances and inequities in health care and research settings.To ensure that engagement is meaningful and not tokenistic, and thus remains ethically conscious, it is important to reflect on the conceptualization of engagement: what is engagement, who is Health Expectations.
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 | 0.065 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.038 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".