Exploring Nurse and Patient Experiences of Developing Rapport During Oncology Ambulatory Care Videoconferencing Visits: Protocol for a Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Telehealth videoconferencing has largely been embraced by health care providers and patients during the COVID-19 pandemic; however, little is known about specific techniques for building rapport and provider-patient relationships in this care environment. Although research suggests that videoconferencing is feasible and can be effective for some types of care, concerns about the impact of technology on provider-patient relationships exist across health disciplines. Suggestions for adapting some in-person rapport techniques, such as the use of small talk, eye contact, and body language to facilitate trust, personal connection, and communication during videoconferencing encounters, have been discussed in the popular press and clinical commentaries. Notably, evidence regarding the effects of these strategies on rapport and clinical care outcomes is lacking. Understanding how to establish rapport in videoconferencing visits is especially important in oncology nursing, where rapport with patients enables nurses to become a source of emotional support, helping patients adapt and navigate the cancer journey. OBJECTIVE: This study aims to investigate the nature of nurse-patient rapport in ambulatory cancer care videoconferencing visits. The objectives include exploring how patients with cancer and nurses describe experiences of rapport and strategies for cultivating rapport in videoconferencing visits and similarities and differences identified by patients with cancer and nurses between experiences of rapport in videoconferencing and in-person visits. METHODS: Semistructured narrative interviews of patients with cancer and nurses will be conducted to understand the experience of rapport building in videoconferencing visits. Nurses and patients will be interviewed separately to facilitate an understanding of the perspectives of both types of participants. Interviews will be conducted on a secure videoconferencing platform. This qualitative descriptive study will describe participant experiences in a manner that, although not without interpretation, is as close to the data as possible. The research team will meet regularly to discuss, define, and document codes, categories, and themes, and the team will maintain a detailed audit trail of analytical decisions. In addition, member checking will enhance the rigor of the study. Nurse and patient interviews will be analyzed separately using identical procedures and may be explored side by side in the final analysis to provide a comparative analysis. Data management and analysis will be performed using NVivo 12. RESULTS: Data collection will begin during summer 2021, with results from the data analysis anticipated by winter 2021. A research team trained in qualitative methodology will use conventional content analysis to analyze the data using first- and second-level codes derived directly from the transcribed text data. CONCLUSIONS: This study aims to determine what behaviors, communication techniques, and relational practices need to be adapted in videoconferencing telehealth visits, setting the foundation for future development of interventions and evidence-based practice guidelines for relationship building during videoconferencing telehealth visits. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27940.
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 | 0,058 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,006 |
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 ».