Cost-effectiveness of a Contact Intervention and a Psychotherapeutic Program for Post-discharge Suicide Prevention
Bibliographic record
Abstract
OBJECTIVE: To determine the cost-effectiveness of 2 strategies for post-discharge suicide prevention, an Enhanced Contact intervention based on repeated in-person and telephone contacts, and an individual 2-month long problem-solving Psychotherapy program, in comparison to facilitated access to outpatient care following a suicide attempt. METHODS: We conducted a cost-effectiveness analysis based on a decision tree between January and December 2019. Comparative effectiveness estimates were obtained from an observational study conducted between 2013 and 2017 in Madrid, Spain. Electronic health care records documented resource use (including extra-hospital emergency care, mortality, inpatient admission, and disability leave). Direct cost data were derived from Madrid's official list of public health care prices. Indirect cost data were derived from Spain's National Institute of Statistics. RESULTS: Both augmentation strategies were more cost-effective than a single priority outpatient appointment considering reasonable thresholds of willingness to pay. Under the base-case scenario, Enhanced Contact and Psychotherapy incurred, respectively, €2,340 and 6,260 per averted attempt, compared to a single priority appointment. Deterministic and probabilistic sensitivity analyses showed both augmentation strategies to remain cost-effective under several scenarios. Enhanced Contact was slightly cost minimizing in comparison to Psychotherapy (base-case scenario: €-196 per averted attempt). CONCLUSIONS: Two post-discharge suicide prevention strategies based on Enhanced Contact and Psychotherapy were cost-effective in comparison to a single priority appointment. Increasing contacts between suicide attempters and mental health-care providers was slightly cost minimizing compared to psychotherapy.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".