Altered cancer care delivery during COVID-19: Evaluating the impact of virtual clinics and treatment changes on oncology patient outcomes, quality of life, and satisfaction.
Notice bibliographique
Résumé
e18618 Background: The coronavirus disease (COVID-19) pandemic has created unprecedented strain on healthcare systems across the world. COVID-19 has thought to have significant impacts on the oncology patient population and has affected their care. Additional research is needed to ascertain the impact of the COVID-19 pandemic at the patient level. We sought to evaluate whether the delivery of cancer care, quality of life (QoL) and treatment outcomes of oncology patients at Mount Sinai Hospital (MSH), Toronto, Canada was impacted by the COVID-19 pandemic. Methods: A 3-part longitudinal questionnaire study including 138 oncology patients receiving active treatment or in active follow-up at MSH was conducted between June 15, 2020 and August 25, 2021. The questionnaire consisted of the EORTC QLQ-C30 (European Organisation for Research and Treatment of Cancer QoL Questionnaire Version 3) and satisfaction with virtual healthcare questionnaire. The questionnaire was completed at baseline (Jun 15-Sep 8, 2020), 1 month follow-up (Jul 15-Oct 8, 2020), and 12 months follow-up (Aug 4-Aug 25, 2021). Repeated measures analysis of variance tests were performed to evaluate EORTC QLQ-C30 subscale score changes and satisfaction with virtual care question scores over time. Results: Overall, the mean EORTC QLQ-C30 QoL scores were seen to improve in oncology patients from 65.1 (SD±22.3) at baseline to 69.1 (SD±16.9) at 12 months follow-up (p = 0.2). Within the EORTC QLQ-C30 functional scales, mean role functioning and mean social functioning scores were observed to increase over 12 months of follow-up, 66.4 to 79.2 (p < 0.05) and 67.7 to 76.4 (p = 0.17), respectively. Little change was observed within other EORTC QLQ-C30 functional scales and individual symptom scales during follow-up. Over 12 months of follow-up, mean agreement (0 = strongly disagree to 6 = strongly agree) to the questionnaire statement regarding avoiding going to the hospital during COVID-19 pandemic had declined, from 4.6 (SD±2.0) at baseline to 3.9 (SD±2.2) at 12 months follow-up (p = 0.09). Although not significant, virtual care satisfaction generally decreased over the follow-up time period. 97% of 48 patients who completed the survey at 12 months of follow-up reported feeling more safe coming into the hospital when considering the current increased vaccination rates in Ontario. Conclusions: As the COVID-19 pandemic has evolved, there has been increased knowledge of disease transmission, along with the introduction of health care measures such as vaccination and treatment. During this time, cancer outpatients at MSH became more comfortable as demonstrated by improvements in both QoL and virtual care scores. Prospective studies should still be considered to assess the efficacy of different methods of improving oncology patient care and QoL during the COVID-19 pandemic.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 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 | 0,000 | 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 ».