Development and Implementation of Postdischarge Text Messages to Adolescents With Suicidal Thoughts and Behaviors Through Caring Contacts: Implementation Study
Notice bibliographique
Résumé
BACKGROUND: Youth suicide is a pressing public health concern, and transitions in care after a suicidal crisis represent a period of elevated risk. Disruptions in continuity of care and emotional support occur frequently. "Caring contacts" validating messages post discharge have the potential to enhance connections with patients and have been shown to improve outcomes. More recently, positive outcomes have been noted using caring contact text messages (SMS and MMS), which hold promise for engaging patients in a pediatric setting, but there are few studies describing the large-scale implementation of such an approach. OBJECTIVE: This study aims to describe the process of developing and implementing automated caring contacts within a quality improvement framework, using a standardized series of supportive texts and images, for adolescents discharged from high-acuity programs at a large midwestern pediatric hospital. We describe lessons learned, including challenges and factors contributing to success. METHODS: We implemented the caring contacts intervention in 3 phases. Phase 1 entailed developing supportive statements and images designed to promote hope, inclusivity, and connection in order to create 2 sets of 8 text messages and corresponding images. Phase 2 included piloting caring contacts manually in the hospital's Psychiatric Crisis Department and Inpatient Psychiatry Unit and assessing the feasibility of implementation in other services, as well as developing workflows and addressing legal considerations. Phase 3 consisted of implementing an automated process to scale within 4 participating hospital services and integrating enrollment into the hospital's electronic medical records. Process outcome measures included staff compliance with approaching and enrolling eligible patients and results from an optional posttext survey completed by participants. RESULTS: Compliance data are presented for 4062 adolescent patients eligible for caring contacts. Overall, 88.65% (3601/4062) of eligible patients were approached, of whom 52.43% (1888/3601) were enrolled. In total, 94.92% (1792/1888) of enrolled participants completed the program. Comparisons of the patients eligible, approached, enrolled, and completed are presented. Primary reasons for eligible patients declining include not having access to a mobile phone (686/1705, 40.23%) and caregivers preferring to discuss the intervention at a later time (754/1705, 44.22%). The majority of patients responding to the optional posttext survey reported that the texts made them feel moderately to very hopeful (219/264, 83%), supported (232/264, 87.9%), that peers would be helped by these texts (243/264, 92%), and that they would like to keep receiving texts given the option (227/264, 86%). CONCLUSIONS: This study describes the successful implementation of automated postdischarge caring contacts texts to scale with an innovative use of images and demonstrates how a quality improvement methodology resulted in a more effective and efficient process. This paper also highlights the potential for technology to enhance care for at-risk youth and create more accessible, inclusive, and sustainable prevention strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».