Managing ‘sick days’ in patients with chronic conditions: An exploration of patient and healthcare provider experiences
Notice bibliographique
Résumé
INTRODUCTION: People with chronic medical conditions often take medications that improve long-term outcomes but which can be harmful during acute illness. Guidelines recommend that healthcare providers offer instructions to temporarily stop these medications when patients are sick (i.e., sick days). We describe the experiences of patients managing sick days and of healthcare providers providing sick day guidance to their patients. METHODS: We undertook a qualitative descriptive study. We purposively sampled patients and healthcare providers from across Canada. Adult patients were eligible if they took at least two medications for diabetes, heart disease, high blood pressure and/or kidney disease. Healthcare providers were eligible if they were practising in a community setting with at least 1 year of experience. Data were collected using virtual focus groups and individual phone interviews conducted in English. Team members analyzed transcripts using conventional content analysis. RESULTS: We interviewed 48 participants (20 patients and 28 healthcare providers). Most patients were between 50 and 64 years of age and identified their health status as 'good'. Most healthcare providers were between 45 and 54 years of age and the majority practised as pharmacists in urban areas. We identified three overarching themes that summarize the experiences of patients and healthcare providers, largely suggesting a broad spectrum in approaches to managing sick days: Individualized Communication, Tailored Sick Day Practices, and Variation in Knowledge of Sick Day Practices and Relevant Resources. CONCLUSION: It is important to understand the perspectives of both patients and healthcare providers with respect to the management of sick days. This understanding can be used to improve care and outcomes for people living with chronic conditions during sick days. PATIENT OR PUBLIC CONTRIBUTION: Two patient partners were involved from proposal development to the dissemination of our findings, including manuscript development. Both patient partners took part in team meetings and contributed to team decision-making. Patient partners also participated in data analysis by reviewing codes and theme development. Furthermore, patients living with various chronic conditions and healthcare providers participated in focus groups and individual interviews.
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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».