Acceptance, Satisfaction, and Preference With Telemedicine During the COVID-19 Pandemic in 2021-2022: Survey Among Patients With Chronic Pain
Notice bibliographique
Résumé
Background The COVID-19 pandemic has forced many health care providers to make changes in their treatment, with telemedicine being expanded on a large scale. An earlier study investigated the acceptance of telephone calls but did not record satisfaction with treatment or patients’ preferences. This warranted a follow-up study to investigate acceptance, satisfaction, and preferences regarding telemedicine, comprising of phone consultations, among health care recipients. Objective The primary aim was to assess the acceptance and satisfaction of telemedicine during the subsequent months of 2021-2022, after the initial wave of the COVID-19 pandemic in Switzerland. Furthermore, we aimed to assess patients’ preferences and whether these differed in patients who had already experienced telemedicine in the past, as well as correlations between acceptance and satisfaction, pain intensity, general condition, perception of telemedicine, and catastrophizing. Finally, we aimed to investigate whether more governmental restrictions were correlated with higher acceptance. Methods An anonymous cross-sectional web-based survey was conducted between January 27, 2021, and February 4, 2022, enrolling patients undergoing outpatient pain therapy in a tertiary university clinic. We conducted a descriptive analysis of acceptance and satisfaction with telemedicine and investigated patients’ preferences. Further, we conducted a descriptive and correlational analysis of the COVID-19 stringency index. Spearman correlation analysis and a chi-square test for categorical data were used with Cramer V statistic to assess effect sizes. Results Our survey was completed by 60 patients. Telemedicine acceptance and satisfaction were high, with an average score of 7.6 (SD 3.3; on an 11-point Numeric Rating Scale from 0=not at all to 10=completely), and 8.8 (SD 1.8), respectively. Respondents generally preferred on-site consultations to telemedicine (n=35, 58% vs n=24, 40%). A subgroup analysis revealed that respondents who already had received phone consultation, showed a higher preference for telemedicine (n/N=21/42, 50% vs n/N=3/18, 17%; χ22 [N=60]=7.5, P=.02, Cramer V=0.354), as well as those who had been treated for more than 3 months (n/N=17/31, 55% vs n/N=7/29, 24%; χ22 [N=60]=6.5, P=.04, Cramer V=0.329). Acceptance of telemedicine showed a moderate positive correlation with satisfaction (rs{58}=0.41, P<.05), but there were no correlations between the COVID-19 stringency index and the other variables. Conclusions Despite high acceptance of and satisfaction with telemedicine, patients preferred on-site consultations. Preference for telemedicine was markedly higher in patients who had already received phone consultations or had been treated for longer than 3 months. This highlights the need to convey knowledge of eHealth services to patients and the value of building meaningful relationships with patients at the beginning of treatment. During the COVID-19 pandemic, the modality of patient care should be discussed individually.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».