81 Family Perceptions of TRaC-K (Telehealth Rounding and Consultation for Kids) a Novel Inpatient Tertiary Regional Virtual Health Collaborative
Notice bibliographique
Résumé
Abstract Background Telehealth Rounding and Consultation for Kids (TRaC-K) is an innovative virtual tertiary-regional collaborative care service for regional pediatric inpatients launched in Southern Alberta in August 2020. TRaC-K uses a mobile audiovisual platform to connect regional and tertiary pediatricians and multidisciplinary clinicians caring for admitted children from Southeastern Alberta. The mobile cart also enables children and families to participate in TRaC-K sessions at the bedside. The TRaC-K model is being evaluated during a 1 year pilot between the Alberta Children’s Hospital (ACH) and the Medicine Hat Regional Hospital (MHRH). Objectives As part of the evaluation of the TRaC-K model, we aimed to explore family perceptions of their experience participating in one or more TRaC-K sessions. Design/Methods Family perceptions of receiving care using the TRaC-K model were explored using qualitative analysis of family interviews. Semi-structured interviews were conducted with consenting families who had participated in one or more TRaC-K sessions while their child was admitted at ACH or MHRH. Interviews were conducted via ZOOM or telephone by a research coordinator experienced in qualitative interviews. A short questionnaire of participant information was completed. A series of questions were asked in addition to clarifying questions. Interviews were recorded, transcribed and the NVivo 12 Pro qualitative data analysis software was used for coding. Inductive thematic analysis was completed by two research team members and three transcriptions were coded by both and compared to ensure alignment in coding methodology. There were 15 transcriptions in total. Themes and subthemes were shared with the research team to validate the assignment of quotes to themes and further organize the themes and subthemes. Results Of the 85 TRaC-K sessions during the one year pilot, 36 sessions included at least one family member participant. Of the family members participating in a TRaC-K session, 15 families completed an interview with contributions from one or more parents/guardians. Thematic analysis identified five themes and several subthemes. Themes included: 1. family centered care, 2. access to care closer to home with a subtheme of ease of transition, 3. enhances quality of care with a subtheme of assessment, 4. communication tool with subthemes of real time, technology, facilitates collaboration and shared decision making, and 5. increases family confidence. Conclusion TRaC-K, a tertiary-regional inpatient virtual health model, was perceived by families to be beneficial to their child’s care and their own experience with inpatient care. Families were able to identify many facets of their experience with this novel model of care. To enhance family centered care for families from outside large urban centers whose children require inpatient admission, virtual health models like TRaC-K that enable tertiary-regional clinician collaboration and family participation should be spread to other pediatric inpatient populations.
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 | 0,007 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,009 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».