Mental Illness and Mental Health: Is the Glass Half Empty or Half Full?
Bibliographic record
Abstract
During the past century, the scope of mental health intervention in North America has gradually expanded from an initial focus on hospitalized patients with psychoses to outpatients with neurotic disorders, including the so-called worried well. The Diagnostic and Statistical Manual of Mental Disorders (DSM), Fifth Edition, is further embracing the concept of a mental illness spectrum, such that increasing attention to the softer end of the continuum can be expected in the future. This anticipated shift rekindles important questions about how mental illness is defined, how to distinguish between mental disorders and normal reactions, whether psychiatry is guilty of prevalence inflation, and when somatic therapies should be used to treat problems of living. Such debates are aptly illustrated by the example of complicated bereavement, which is best characterized as a form of adjustment disorder. Achieving an overarching definition of mental illness is challenging, owing to the many different contexts in which DSM diagnoses are used. Careful analyses of such contextual utility must inform future decisions about what ends up in DSM, as well as how mental illness is defined by public health policy and society at large. A viable vision for the future of psychiatry should include a spectrum model of mental health (as opposed to exclusively mental illness) that incorporates graded, evidence-based interventions delivered by a range of providers at each point along its continuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".