Psychological distress in patients with cancer at the Kenyatta National Hospital in Nairobi, Kenya, during the COVID-19 pandemic
Notice bibliographique
Résumé
Abstract Background: Psychosocial care for oncology patients is now recognized as a critical aspect of care because it has a positive impact on patient outcomes. Various screening tools have been validated to objectively measure the levels of distress, such as the National Comprehensive Cancer Network distress thermometer. However, there is little evidence of its use in sub-Saharan Africa, where the cancer burden continues to increase. This study sought to evaluate the levels of psychological distress in patients with cancer and the impact of the COVID-19 pandemic. Methods: This was a single-center cross-sectional study among patients with a histological diagnosis of cancer attending the hemato-oncology and radio-oncology units at the Kenyatta National Hospital, a referral tertiary center. We used the National Comprehensive Cancer Network Distress Thermometer and Problem Checklist to define psychological distress, fear of COVID-19 scale, and Corona Anxiety Score to determine the level of fear and anxiety caused by COVID-19 given the study happened during the pandemic, and the Eastern Cooperative Oncology Group (ECOG) to assess the performance status. Results: Of the 361 patients, the median age was 54 years (interquartile range, 43–63), and most were female (70%). The leading cancer diagnosis was breast cancer (26%), followed by cervical cancer (24%), with most of the patients having advanced disease and 28% having ECOG 3. Most (80%) patients were able to continue with their treatment despite the COVID-19 pandemic; however, 71% had a high level of fear of COVID-19 but minimal anxiety symptoms based on Corona Anxiety Score. The mean distress thermometer score was 2.7 (SD, 2.6), with 30% having a high level of distress (4 or above). ECOG status was the only variable significantly associated with high levels of distress, with the strongest association observed in the highest ECOG status (ECOG 4: OR, 6.8 [95% CI, 2.8–16.6] P < .001). Transportation was the main problem in the practical domain (62%) while fears and worries in the emotional domain (46% and 49%, respectively), and pain (65%) were the main physical problems. Conclusions: One-third of patients experienced high levels of distress. These patients reported significant concerns, such as transportation, fears, worry, and pain, in the problem checklist. There is a need to incorporate screening for distress into our patient population to help identify these patients and institute appropriate interventions.
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 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 | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».