Perceived usefulness of new technologies in palliative care volunteering: mixed-methods study with stakeholders
Notice bibliographique
Résumé
BACKGROUND: During the COVID-19 pandemic, face-to-face volunteer support for patients and families was not possible, making it necessary to explore alternative ways of reducing distress. New technologies emerged as a valuable resource, facilitating communication and information exchange, and supporting volunteer tasks. OBJECTIVE: To explore the perceived usefulness of new technologies in volunteering among different stakeholders (patients, relatives, professionals, and volunteers), and to examine how these perceptions relate to participants' technological profiles. DESIGN: A cross-sectional mixed-methods study was conducted to explore attitudes and preferences toward new technologies. Quantitative data were analyzed descriptively and through regression models, while qualitative data were examined using thematic analysis. METHODS: Participants were recruited through consecutive non-probabilistic sampling and included patients, relatives, healthcare professionals, and volunteers from various care settings. Quantitative measures assessed perceptions of usefulness, benefits, barriers, and satisfaction with volunteering, alongside the TechPH tool to profile technological attitudes. Qualitative data were collected through interviews and focus groups using open-ended questions to explore the perceived usefulness of new technologies in palliative care volunteering. Quantitative analysis involved descriptive statistics, Pearson correlations, ANOVA, and multiple linear regression. Qualitative data were analyzed using thematic analysis. RESULTS: A total of 402 individuals participated: 50 patients, 45 relatives, 136 professionals and 171 volunteers. Perceived usefulness of new technologies varied: 50% of patients, 63.6% of relatives, 77.8% of professionals, and 78.2% of volunteers found them beneficial. Three themes emerged from qualitative analysis: difficulties in new technologies use (mainly among patients), perceived benefits (e.g., enhanced communication), and the need for volunteer training in digital skills. CONCLUSIONS: All stakeholder groups recognized new technologies as useful for volunteer support in palliative care, with the highest perceived usefulness among professionals and volunteers. However, professionals were the least involved in volunteer support. Patients reported the lowest acceptance, preferring a hybrid model in which technology complements, but does not replace, in-person support.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».