Improving Health and Well-Being of People With Post–COVID-19 Consequences in South Africa: Situation Analysis and Pilot Intervention Design
Notice bibliographique
Résumé
Background: Multisystemic complications post-COVID-19 infection are increasingly described in the literature, yet guidance on the management remains limited. objectives: This study aimed to assess the needs, preferences, challenges, and existing interventions for individuals with post-COVID-19 symptoms. Based on this, we aimed to develop a context-adapted intervention to improve the overall health and well-being of individuals with post-COVID-19 complications. Methods: We conducted a cross-sectional mixed-methods situation analysis assessing the needs, preferences, challenges, and existing interventions for patients with post-COVID-19 symptoms. We collected data through questionnaires, semistructured in-depth interviews, and focus group discussions (FGDs) from individuals diagnosed with COVID-19 within the previous 18-month period and health care providers who managed patients with COVID-19 in both inpatient and outpatient settings. Quantitative data were summarized using descriptive statistics, qualitative data were transcribed, and deductive analysis focused on suggestions for future interventions. Findings guided the development of a group intervention. Results: We conducted 60 questionnaires, 13 interviews, and 3 FGDs. Questionnaires showed limited knowledge of post-COVID-19 complications at 26.7% (16/60). Of those who received any rehabilitation for COVID-19 (19/60, 31.7%), 94.7% (18/19) found it helpful for their recovery. Just over half (23/41, 56%) of those who did not receive rehabilitation reported that they would have liked to. The majority viewed rehabilitation as an important adjunct to post-COVID-19 care (56/60, 93.3%) and that support groups would be helpful (53/60, 88.3%). Qualitative results highlighted the need for mental health support, structured post-COVID-19 follow-up, and financial aid in post-COVID-19 care. Based on the insights from the situation analysis, the theory of change framework, and existing post-COVID-19 evidence, we designed and conducted a pilot support group and rehabilitation intervention for individuals with post-COVID-19 complications. Our main objective was to assess the change in physical and psychological well-being pre- and postintervention. The intervention included 8 weekly themed group sessions supplemented by home tasks. Effectiveness of the intervention was evaluated by questionnaires pre- and postintervention on post-COVID-19 symptoms, quality of life with the EuroQoL 5-Dimension 5-Level, short Warwick-Edinburgh Mental Wellbeing Scale, and physical function by spirometry and 1-minute sit-to-stand test. We also assessed the feasibility and acceptability of the intervention by questionnaires and semistructured in-depth interviews. The intervention outcome analysis is yet to be conducted. Conclusions: Insights from patients and health care providers on the characteristics of post-COVID-19 complications helped guide the development of a context-adapted intervention program with potential to improve health and well-being post-COVID-19.
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 | 0,009 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».