Factors Associated with Adherence to Antiretroviral Therapy among Clients Aged Eighteen Years and above Attending Opportunistic Infection Clinic at Zvishavane District, Zimbabwe
Notice bibliographique
Résumé
Strict adherence to antiretroviral therapy (ART) is key to sustained HIV suppression, reduced risk of drug resistance, improved overall health, quality of life, and survival. The purpose of the study was to examine the relationship between social support, socio-demographic factors, client related factors, health providers and regime related factors and adherence to ART among HIV positive clients attending opportunistic infections (OI) clinics in Zvishavane District. A descriptive correlational study was conducted with a convenience sample of 81 participants. Permission to conduct the study was sought from respective ethical review boards. Participants gave written informed consent. Data was collected using a structured questionnaire from March to April 2010. Interviews were carried out in a private room and each lasted about 30 minutes. Code numbers appeared on completed questionnaires which were kept by the researcher in a lockable cupboard. Data was analysed using SPSS version 12. Descriptive statistics were used to analyze data on demographics and levels of adherence to ART and social support. Inferential statistics (Pearson’s correlation [r]) were used to examine the relationship between social support and level of adherence to ART. Nineteen participants (23%) were male while 62 (77%) were female. Ages ranged from 18 to 65 years. Fifty-eight (71%) had high adherence to ART while 50 (61.7%) had moderate level of social support. There was a very weak positive correlation between social support and adherence to ART (r = .165), a negative significant correlation between worry and adherence (r = -.366 p<.01), a weak positive correlation between income and adherence (r = .248 p<.05) and a weak positive correlation between ability to pay user fees and adherence (r = .266 p<.05). Social support tended to increase with adherence to ART. Worry increased as adherence decreased. As income increased adherence also increases. There is need for comprehensively assessment of clients before commencement on ART to address factors that might negatively affect adherence.
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 | 0,002 | 0,003 |
| 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 | 0,001 |
| 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) | 0,001 | 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 ».