Pressures to adhere to treatment (‘leverage’) in English mental healthcare
Bibliographic record
Abstract
BACKGROUND: Coercion has usually been equated with legal detention. Non-statutory pressures to adhere to treatment, 'leverage', have been identified as widespread in US public mental healthcare. It is not clear if this is so outside the USA. AIMS: To measure rates of different non-statutory pressures in distinct clinical populations in England, to test their associations with patient characteristics and compare them with US rates. METHOD: Data were collected by a structured interview conducted by independent researchers supplemented by data extraction from case notes. RESULTS: We recruited a sample of 417 participants from four differing clinical populations. Lifetime experience of leverage was reported in 35% of the sample, 63% in substance misusers, 33% and 30% in the psychosis samples and 15% in the non-psychosis sample. Leverage was associated with repeated hospitalisations, substance misuse diagnosis and lower insight as measured by the Insight and Treatment Attitudes Questionnaire. Housing leverage was the most frequent form (24%). Levels were markedly lower than those reported in the USA. CONCLUSIONS: Non-statutory pressure to adhere to treatment (leverage) is common in English mental healthcare but has received little clinical or research attention. Urgent attention is needed to understand its variation and place in community practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".