The shifting landscape of private healthcare providers before and during the COVID-19 pandemic: Lessons to strengthen the private sectors engagement for future pandemic and tuberculosis care
Notice bibliographique
Résumé
INTRODUCTION: COVID-19 pandemic changed many aspects of healthcare services and deliveries, including among private healthcare providers (i.e., private healthcare facilities [HCFs] and private practitioners [PPs]). We aimed to compare the spatial distribution of private providers and describe changes in characteristics and services offered during and before the COVID-19 pandemic, and explore the tuberculosis (TB) and COVID-19-related services offered by the private sector in Bandung, Indonesia. METHODS: A cross-sectional study with historical comparison was conducted in 36 randomly selected community health centers areas (locally referred to as Puskesmas) in Bandung, Indonesia, during the COVID-19 pandemic from 5th April 2021 - 27th December 2021. Data pertaining to before the COVID-19 pandemic was abstracted from a similar survey conducted in 2017 (i.e., INSTEP study). We obtained latitude and longitude coordinates of private healthcare providers and then compared the geographical spread with data collected for INSTEP study. We also compared characteristics of, and services provided by private healthcare providers interviewed during the COVID-19 pandemic with those previously interviewed for INSTEP study. Differences were summarized using descriptive and bivariate analyses. RESULTS: From April-December 2021, we surveyed 367 private HCFs and interviewed 637 PPs. Compared to INSTEP study data, the number of operating HCFs was reduced by 3% during the COVID-19 pandemic (401 vs. 412 before COVID-19), although we observed increases in laboratory service (37.8% increase), x-ray service (66.7% increase), and pharmacy (18.1% increase). Among a subset of private HCFs managing patients with respiratory tract infection symptoms, a quarter (60/235, 25.3%) indicated that they had to close their facilities in response to the emerging situation during the COVID-19 pandemic. For PPs, the number of practicing PPs was reduced by 7% during the COVID-19 pandemic (872 vs. 936 before COVID-19). Interestingly, the number of practicing PPs encountering patients with TB disease increased during the COVID-19 pandemic (42.9% vs. 35.7% before COVID-19, p = 0.008). CONCLUSION: This study confirmed that the COVID-19 pandemic adversely impacted health care service deliveries in private sectors, largely marked by closures and shortened business hours. However, the increased service capacities (laboratory and pharmacy), as well as significant increase in the number of patients cared for TB disease by PPs during the COVID-19 pandemic, made a more compelling case to further the implementation of public-private mix model for TB care in Indonesia.
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 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».