Breast Cancer knowledge, perceptions and practices in a rural Community in Coastal Kenya
Notice bibliographique
Résumé
BACKGROUND: Data on breast healthcare knowledge, perceptions and practice among women in rural Kenya is limited. Furthermore, the role of the male head of household in influencing a woman's breast health seeking behavior is also not known. The aim of this study was to assess the knowledge, perceptions and practice of breast cancer among women, male heads of households, opinion leaders and healthcare providers within a rural community in Kenya. Our secondary objective was to explore the role of male heads of households in influencing a woman's breast health seeking behavior. METHODS: This was a mixed method cross-sectional study, conducted between Sept 1st 2015 Sept 30th 2016. We administered surveys to women and male heads of households. Outcomes of interest were analysed in Stata ver 13 and tabulated against gender. We conducted six focus group discussions (FGDs) and 22 key informant interviews (KIIs) with opinion leaders and health care providers, respectively. Elements of the Rapid Assessment Process (RAP) were used to guide analysis of the FGDs and the KIIs. RESULTS: A total of 442 women and 237 male heads of households participated in the survey. Although more than 80% of respondents had heard of breast cancer, fewer than 10% of women and male heads of households had knowledge of 2 or more of its risk factors. More than 85% of both men and women perceived breast cancer as a very serious illness. Over 90% of respondents would visit a health facility for a breast lump. Variable recognition of signs of breast cancer, limited decision- autonomy for women, a preference for traditional healers, lack of trust in the health care system, inadequate access to services, limited early-detection services were the six themes that emerged from the FGDs and the KIIs. There were discrepancies between the qualitative and quantitative data for the perceived role of the male head of household as a barrier to seeking breast health care. CONCLUSIONS: Determining level of breast cancer knowledge, the characteristics of breast health seeking behavior and the perceived barriers to accessing breast health are the first steps in establishing locally relevant intervention programs.
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 | 0,000 |
| 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 | 0,001 |
| 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 ».