Patient Living With Chronic Illness Perception of Interprofessional Collaboration in a Telehealth Context in Primary Care: Protocol for a Qualitative Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: Background: The enhancement of Primary care and the prevalence of chronic diseases are key issues worldwide, especially in Canada. The rising incidence of chronic illnesses, now the leading cause of mortality worldwide, creates complex challenges that can compromise the quality of care provided to patients. The lack of communication directly affects relational continuity, i.e., the sharing of information from previous events and circumstances, to ensure that care is appropriate to the individual and his or her problem. These challenges highlight the importance of establishing clear patient pathways within interprofessional teams, ensuring that information is shared efficiently, and that the continuity of care is coordinated effectively, especially in a telehealth context. Since 2019, telehealth has become an essential tool for patient with chronic disease, though often implemented with no specific infrastructure. Interprofessional collaboration plays a critical role in the use of telehealth in managing chronic diseases. OBJECTIVE: Objectives: This study aims to understand the interprofessional collaboration (IPC) process as experienced by patients in a telehealth context within primary care, with a focus on patient engagement. More specifically, the study's objectives are: 1) to describe the IPC process in telehealth within primary care from the perspective of patients living with chronic conditions; 2) to identify, in collaboration with patients living with chronic disease, the barriers and facilitating factors of this process; 3) to understand the engagement of these patients in relation to the IPC process in a telehealth context. METHODS: Methods/design: To describe the process of interprofessional collaboration in the telehealth context in primary care from the perspective of patients living with chronic disease, this qualitative research is based on a constructivist research methodology. The research team constructs knowledge derived from the interpretation of information that was obtained during the interviews with participants. To meet the study's objectives, a qualitative journey mapping data collection was carried out, following the approach of Trebbel et al., (2010). Individual interviews were analyzed iteratively. This method is useful for this research as it visually and collaboratively captures patients lived experiences. RESULTS: Results: Data collection was completed between the end of May 2024 and November 2024. A total of 22 interviews were conducted. The project is currently in progress, with multiple papers being drafted for publication in peer reviewed journals. CONCLUSIONS: Conclusion: The results of this study will support and improve the interprofessional collaboration process in the telehealth context by providing concrete insights into patients' experiences, identifying gaps and strengths in current collaborative practices, and offering evidence-based recommendations. Journey mapping will help identify potential facilitating factors for improving primary care in the telehealth context according to the patient's journey. Results will be used to build a practical guide (in phase 2) supporting interprofessional collaboration in the primary care telehealth context. .
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 | 0,054 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,005 |
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 ».