Good Mental Health Care: What It Is, What It Is Not & What It Could Be
Bibliographic record
Abstract
Abstract What makes for good mental health care? What are the barriers to good care and, when they can be overcome, what accounts for successful treatment? What does successful treatment and care, in fact, mean? Can they mean different things to different people? If so, how can we think about them in a practical way that is useful to patients, families, and clinicians? On the one hand, from work infields as various as neuroscience, clinical psychology, and anthropology, we are learning (and rediscovering) more and more about how the human mind works and the many ways that psychological suffering can be preempted and treated. On the other hand, in many ways, the mental health care system is either dysfunctional or working against what we know to be best for psychological and social flourishing-the disappearance, for example, of true “care” from medical and mental health care systems. In this essay, set against the background of diverse perspectives provided by the foregoing essays in this volume, we attempt to frame and address some of these basic questions, giving priority to practical, down-to-earth, lay, and professional considerations.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".