Introduction: The Politics of Resilience and Recovery in Mental Health Care
Bibliographic record
Abstract
Recovery and resilience are now two of the central frameworks for organizing mental health care in the Western world. These frameworks posit that mental health "patients" can recover from their illnesses, and that resilience may be developed as a strength in order to avert or prevent so-called mental illness from the outset. The turn to "recovery" and to "resilience" has occurred in a context wherein mental health governance models based centrally on institutionalization had been the subject of much political resistance from those who have been psychiatrized, and also in a context of the retrenchment of state services through neo-liberal restructuring and cost-cutting measures. Large-scale deinstitutionalization in the second half of the 20th Century was met with the development of "Community-based" care as an alternative. Currently, those negotiating mental health services often find themselves subject to a mixture of institutional and community based mental health services, as well as other secondary institutional systems that offer mental health interventions (universities, work places, primary education, etc). Although such shifts apparently respond to the concerns expressed in the political resistance directed at total institutions (see Simultaneously Western states are downloading their social responsibilities to the voluntary sector and to citizens themselves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".