Prevalence and Predictors of Complementary and Alternative Medicine (CAM) Use Among Health Workers in Nigeria
Notice bibliographique
Résumé
BACKGROUND: The use of complimentary and alternative medicines has risen globally. We therefore, explored the prevalence and predictors of use of complementary and alternative medicines among healthcare workers. METHODS: This was a cross-sectional study that was conducted between 1st June and 31st August 2018 on the use of complementary and alternative medicines among health workers in Federal Medical Center Makurdi and Benue State University Teaching Hospital, Makurdi in Benue State. Questionnaire was used to collect data from respondents and data analysed using logistic binary regression models. RESULT: Response rate for the study was 80.2% out of which females were 196 (58.2%) with 215 (65.7%) in the age bracket of 31 – 60 years. Married respondents were 244 (72.4%) while Medical Doctors followed by Nurses were 87 (25.8%) and 84 (24.9%) respectively. Majority of the respondents, 113 (33.8%) have a monthly salary of above N100,000 (277.8 USD @ exchange rate of N360) while health workers of Tiv ethnic extraction had the highest number of 202 (60.7%) followed by those of Idoma extraction, 95 (28.5%). Those with years of work experience between (0 -15) were 268 (87.9%). The most used CAM was spiritual therapy, 230 (68.2%) while whole-body therapy was the least with 84 (24.9%). Use of biological therapy and manipulative therapy were 182 (54%) and 207 (61.4%) respectively. The odds of a female health worker using spiritual therapy was more than twice that of their male counterpart, (AOR: 2.218, 95% CI: 1.391 – 3.538). The odds of a Community Health Extension Worker and a medical doctor using a biological therapy among the study population were four times and almost thrice respectively compared to a pharmacist (AOR: 4.117, 95% CI: 1.690 – 10.030) and (AOR: 2.541, 95% CI: 1.095 – 5.896). The odds of an Idoma health worker using a manipulative and body-based therapy was thrice that of a Tiv health worker (AOR: 3.00, 95% CI: 1.318 – 6.829). While the odds of a Tiv health worker using whole-body therapy was seven times that of Idoma (AOR: 7.420, 95% CI: 2.186 – 25.188. CONCLUSION: There was high prevalence of CAM use by health workers and this has potentials to influence integration of CAM with conventional medicines.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| 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 ».