Embedding lived experience into mental health academic research organizations: Critical reflections
Bibliographic record
Abstract
BACKGROUND: As part of a growing emphasis on engaging people with lived experience of mental health conditions in mental health research, there are increasing calls to consider and embed lived experience throughout academic research institutes. This extends beyond the engagement of lay patients and also considers the potential roles of academic researchers with lived experience. When the lived experience of academic researchers is applied to academic work, there is the potential to improve the relevance of the research, while destigmatizing mental illness within academia. However, there are different and often contrasting perspectives on the way a lived experience academic researcher initiative should be implemented. OBJECTIVES: This article describes some of the key issues to be considered when planning an initiative that leverages and values the lived experience of academic researchers, including the advantages and disadvantages of each potential approach. DISCUSSION & RECOMMENDATIONS: Institutions are encouraged to reflect on the ways that they might support and value lived experience among academic researchers. In developing any such initiative, institutions are encouraged to be transparent about their objectives and values, undertake a careful planning process, involve researchers with lived experience from the outset and consistently challenge the stigma experienced by academic researchers with lived experience. PATIENT OR PUBLIC CONTRIBUTION: Multiple authors are academic researchers with lived experience of mental health conditions.
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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 | 0.173 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.055 | 0.120 |
| Scholarly communication | 0.039 | 0.037 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.015 | 0.042 |
| 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".