Mental Health Retrosight: Understanding the returns from research (lessons from schizophrenia): Policy Report
Bibliographic record
Abstract
This study examines the impacts arising from neuroscience and mental health research going back 20-25 years, and identifies attributes of the research, researchers or research setting that are associated with translation into patient benefit, in the particular case of schizophrenia. The study combined two methods: forward-tracing case studies to examine where scientific advances of 20 years ago have led to impact today; and backward-tracing perspectives to identify the research antecedents of today's interventions in schizophrenia. These research and impact trails are followed principally in Canada, the UK and the USA. The headline findings are as follows: The case studies and perspectives support the view that mental health research has led to a diverse and beneficial range of academic, health, social and economic impacts over the 20 years since the research was undertaken.Clinical research has had a larger impact on patient care than basic research has over the 20 years since the research was undertaken.Those involved in mental health research who work across boundaries are associated with wider health and social benefits.Committed individuals, motivated by patient need, who effectively champion research agendas and/or translation into practice are key in driving the development and implementation of interventions.This study provides an overview of the methods and presents the full set of findings, with the policy provocations they raise, and an emerging research agenda. It has been written for funders of biomedical and health research and health services, health researchers, and policymakers in those fields. It will also be of interest to those involved in research and impact evaluation.
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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 | 0.062 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.034 | 0.020 |
| 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".