Harnessing primary care to enhance recovery from severe mental illness
Bibliographic record
Abstract
Governments across the English-speaking world have stated that mental health services for people with severe mental illness (SMI) must focus on the redefined notion of recovery. In what has become the seminal definition, Anthony states that: 'Recovery is a way of living a satisfying, hopeful, and contributing life. Recovery involves the development of new meaning and purpose in one's life as one grows beyond the catastrophic effects of psychiatric disability.' 2 This emphasis on recovery derives from evidence that SMI is not necessarily a lifelong, chronic, and disabling condition. On the contrary, people with SMI can make an excellent recovery. umerous national mental health strategies, including those of England, Canada, and Australia, recommend that GPs and primary health care could and should play a greater role in enhancing recovery. The mental health strategy for England has 'an ambitious aim to mainstream mental health in England', stating that local GP consortia should provide and/or commission high-quality mental health care, as well as taking action to reduce the multiple physical comorbidities frequently afflicting those with SMI. More specifically it states that action should be taken to 'integrate recovery approaches into primary care'. rake and Whitley recently argued that a shift in continuing care from tertiary and secondary care to primary care for people with SMI would be entirely consistent with the philosophical and ethical underpinnings of the recovery paradigm. They contend that recovery by definition involves living an everyday normative life in the community. Hence, separation into specific mental hospitals and ghettoised services is inconsistent with recovery, as it perpetuates segregation and perceived 'difference'. A shift in service delivery towards primary care could thus reduce the social exclusion and stigma frequently felt by people with SMI. Indeed, this is noted in the mental health strategy for England, which acknowledges the 'institutionalised discrimination inherent in many organisations, including support services'.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".