Bibliographic record
Abstract
In 2003, 25 years after Rosalynn Carter chaired the first Presidential Commission on Mental Health, she testified before the New Freedom Commission on Mental Health, chaired by Michael Hogan.1 When asked what the greatest advance had been in the intervening years, she said it was adopting the belief that people with serious mental illness could recover. As heterogeneous as people with schizophrenia are, so too are their paths to recovery. Recovery may proceed along multiple domains: psychotic symptoms, cognitive capacities, functioning in terms of independent living in the community, competitive employment, social and intimate relationships (“a home, a job and a date on the weekend”), physical health, economic health, and other aspects of quality of life.2 To the extent we recognize and respond to the diverse domains of a person's life, we will help people in the work of crafting a life. We comment on this series of reports describing the challenges of measuring recovery from schizophrenia and identifying predictors of recovery. We offer these comments as public mental health system administrators charged with promoting recovery, including knowing whether the services being purchased with public funds are promoting recovery. Such knowledge requires measurement. Is the intervention being carried out with fidelity? As both administrators and as evaluators/researchers, we look to our colleagues in the field to offer measurement tools of immediate practical significance to consumers and clinicians.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".