Evaluation of Diagnostic Imaging Capacity and the Role for Point-of-Care Ultrasound (POCUS) within the Zanzibar Health System
Notice bibliographique
Résumé
Background: The Zanzibar Ministry of Health identified access to ultrasound (US) as a system priority due to limited diagnostic imaging capacity and consequent impact on patient care and requested a needs assessment in this regard. As a result, the objective of this study was to assess diagnostic imaging capacity focusing on ultrasound in Zanzibar, including health care providers’ (HCPs) current training, use, and barriers to implementation. Methods: A previously published ultrasound needs assessment survey was modified and administered on-site at the eight public hospitals in Zanzibar among a convenience sample of HCPs. Demographics, perceived US needs, current training and practice, and availability of specialty support were assessed. HCPs also completed focused personal interviews (FPIs) to explore experience with training, interests, and barriers to ultrasound. On-site diagnostic imaging modalities were assessed. Results: There were nine ultrasound machines present at six of the eight public hospitals assessed. All had x-ray, but only one had a CT scanner and a radiologist. There was no MRI capacity at the hospitals assessed at the time of the study. Survey data among 40 participants revealed that prior experience with POCUS was limited with only 10% reporting any prior ultrasound training or experience. The majority of those surveyed (72%), indicated a ‘high’ interest in learning ultrasound. Of those reporting interest in POCUS applications, obstetrics was the most often cited (70%). Lack of ultrasound machines (40%) and educators (28%) were identified as the greatest barriers. Conclusion: HCPs in Zanzibar have limited access to diagnostic imaging, including ultrasound, and expressed a high level of interest in learning point-of-care ultrasound. A shortage of machines and educators are the main barriers to widespread use. Obstetrics is the application for which ultrasound is currently most used and is the application HCPs are most interested in learning. Equipment and educational support for a POCUS program could improve care by increasing access to diagnostic imaging.
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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».