Description and Initial Evaluation of a Postdischarge Intervention to Support Transition of Care in Substance Use Disorder Treatment
Bibliographic record
Abstract
ABSTRACT Objectives: This paper describes an interdisciplinary outpatient service developed during the COVID-19 pandemic to support and ensure continuity of care for patients with substance use and concurrent mental health disorders. Methods: This service provides 2 weeks of intensive mental health and addiction support to patients recently discharged from the hospital. Here we describe the formation of the service, data on service utilization, and initial qualitative evaluation of the patient experience with this service. Results: Patients accessing this service had high rates of treatment engagement, with an average of 5.6 visits per patient, and were most commonly receiving treatment for alcohol (68.0%), stimulant (47.7%), and opioid (37.3%) use disorders. Patients reported positive experiences with the service, valuing the high frequency and low-barrier contact. Conclusions: Interdisciplinary teams providing brief and intensive interdisciplinary support to patients with substance use disorders after inpatient hospitalizations are feasible and well-received by patients. Objectifs: Cet article décrit un service ambulatoire interdisciplinaire mis en place pendant la pandémie de COVID-19 pour soutenir et assurer la continuité des soins pour les patients souffrant de toxicomanie et de troubles mentaux concomitants. Méthodes: Ce service offre deux semaines de soutien intensif en matière de santé mentale et d’addiction à des patients récemment sortis de hôpital. Nous décrivons ici la création du service, des données sur l’utilisation du service et l'évaluation qualitative initiale de l’expérience des patients avec ce service. Résultats: Les patients qui accèdent à ce service ont des taux élevés d’engagement dans le traitement, avec une moyenne de 5,6 visites par patient et reçoivent le plus souvent un traitement pour un trouble lié à l’usage d’alcool (68,0%), de stimulants (47,7%) et d’opioïdes (37,3%). Les patients ont fait part d’expériences positives avec le service, appréciant la fréquence élevée et les contacts à faibles barrières. Conclusions: Les équipes interdisciplinaires fournissant un soutien interdisciplinaire bref et intensif aux patients souffrant de troubles liés à l’utilisation de substances psychoactives après une hospitalisation sont réalisables et bien accueillies par les patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".