Exploratory Use of Cloud Computing and Social Media for Prostate Cancer Advocacy in Nigeria
Notice bibliographique
Résumé
Background and context: Prostate cancer is the commonest cancer affecting Nigerian men, with worse outcome compared with men from the developed world. There is limited public awareness about prostate cancer in Nigeria. Oga Blue 4 Prostate Awareness (OB4PA) was created by a consortium of Nigerian nonprofits for prostate cancer advocacy (PCA). Aims: -Design PCA using videos, printed brochure and social media -Implement PCA in five Nigerian states -Evaluate the reach and impact of PCA campaign Program/Policy process: Community-based participatory process, involving the medical community, prostate cancer survivor, and the public was used. Multimedia teaching was used to enhance learning and retention; social media was used to engage groups and individuals. Content development involved iterative consultation among project leaders, medical experts and target audience, often on social media. High-quality teaching videos were recorded in English and Nigerian Pidgin languages. Videos ensured consistency and ease of broadcast. Videos were accessed by OB4PA partners through cloud computing (OneDrive). Facebook was used to promote the campaign, engage local audience, and for Facebook Live presentation. Local health professionals projected the video in appropriate language to audiences in religious and community groups. Brochure detailing clinical features and local service providers was distributed. Audience evaluation was obtained following each interaction. Outcomes: In 6 weeks, 20 presentations were made to 1800 persons. The Facebook Live presentation had 1500 views, reached 9302 people and was shared 107 times. A total of 25 Facebook posts were made, resulting in 628 like, 1908 video views, 160 shares, and reached 14,222 people. Almost all participants had positive feedback on the free and detailed advocacy. Most questions focused on the causes and prevention of prostate cancer, especially on the use of nutritional supplements. What was learned: Cloud computing enabled us to have one presenter; this eliminated the need to find a presenter for each organization. Audience appreciated simplified videos used in explaining the disease process and need for personalized early detection. Facebook live presentation attracted the most reactions on social media, with most comments showing that people liked the intervention. Audience feedback showed that adding advocacy cellphone video by a survivor helped demystify prostate cancer. Having the main presentation video in different file formats and sizes enhanced sharing on social media, as most Nigerians access the Internet on cell phones. Reliable access to projectors was challenging, especially in remote areas. Overall, use of cloud computing and social media were crucial in the success of the PCA project. Lessons from OB4PA informed the design of the current We Can, I Can Conquer Cervical Cancer Awareness project in Nigeria.
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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 | 0,001 | 0,004 |
| 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) | 0,000 | 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 ».