Implementation of caring contacts using patient feedback to reduce suicide‐related outcomes following psychiatric hospitalization
Bibliographic record
Abstract
INTRODUCTION: Suicide risk is substantially elevated following discharge from a psychiatric hospitalization. Caring Contacts (CCs) are brief communications delivered post-discharge that can help to improve mental health outcomes. METHOD: This three-phase, mixed-method quality-improvement study revised an existing CC intervention using iterative patient and community feedback. Inpatients (n = 2) and community members (n = 13) participated in focus groups to improve existing CC messages (phases 1 and 2). We piloted these messages among individuals with a suicide-related concern following discharge from an inpatient psychiatric hospitalization (n = 27), sending CCs on days 2 and 7 post-discharge (phase 3). Phase 3 participants completed mental health symptom measures at baseline and day 7, and provided feedback on these messages. RESULTS: Phase 1 and 2 focus group participants indicated preferences for shorter, more visually appealing messages that featured personalized, recovery-focused content. Phase 3 participants demonstrated reductions in depressive symptoms at day-7 post-discharge (-6.4% mean score on Hopkins-Symptom-Checklist, -9.0% mean score on Entrapment-Scale). Most participants agreed that CC messages helped them feel more connected to the hospital and encouraged help-seeking behavior post-discharge. CONCLUSION: This study supports the use of an iterative process, including patient feedback, to improve CC messages and provides further pilot evidence that CC can have beneficial effects.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".