Outcomes and feasibility of the short transitional intervention in psychiatry in improving the transition from inpatient treatment to the community: A pilot study
Bibliographic record
Abstract
Discharge from psychiatric inpatient care is frequently described as chaotic, stressful, and emotionally charged. Following discharge, service users are vulnerable to becoming overwhelmed by the challenges involved in readapting to their home environments, which could result in serious problems and lead to readmission. The short transitional intervention in psychiatry (STeP) is a bridging intervention that includes pre- and post-discharge sections. It aims to prepare patients for specific situations in the period immediately following discharge from a psychiatric hospital. We conducted a quasi-experimental pilot study to determine the feasibility of the intervention, and gain insight into the effects of the STeP. Two inpatient wards at a Swiss psychiatric hospital participated in the study, and represented the intervention and control arms. Patient recruitment and baseline assessment were performed 2 weeks prior to discharge. Follow-up data were collected 1 week subsequent to discharge. Questionnaires measured coping, admission and health-care usage, self-efficacy, working alliance, experience of transition, and the number of difficulties experienced following discharge. Fourteen and 15 patients completed the follow-up assessment in the control and intervention groups, respectively. The STeP did not affect primary or secondary outcomes; however, it was shown to be feasible, and patients' feedback highlighted the importance of post-discharge contact sessions. Further research is required to improve understanding of the discharge experience, identify relevant patient outcomes, and assess the effectiveness of the intervention in an adequately-powered randomized, controlled trial.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".