Shifting neurotypical prevalence in knowledge production about the mentally diverse: A qualitative study exploring factors potentially influencing a greater presence of lived experience-led research
Bibliographic record
Abstract
Research which is led by mentally diverse persons offers a variety of benefits. Crucially, this research holds potential to target wide-ranging social inclusion issues. Recognizing that these studies cannot lay claim to be commonplace, the aim of this investigation is to inform and improve policy supportive of lived experience-led studies by critically investigating evidence-based factors influencing a greater presence of this genuinely inclusive style of research. Following purposive sampling, thematic analysis was applied to twelve articles meeting with inclusion criteria and retrieved from Scopus, Medline, PsycINFO and ProQuest databases. This investigation reveals three key findings. First, this exploratory study identifies factors supporting and resisting lived experience-led research across micro, meso and macro levels. Second, investment in future research is needed to identify evidence-based measures with capacity to redress factors constraining opportunities for mentally diverse persons to develop research careers and to potentially lead the way in reforming mental health and other services. Finally, any assertions of neurodiverse researchers as necessarily being lacking in professional qualifications or reliant upon the assistance of neurotypical colleagues should be critically questioned.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".