Exploratory Use of Cloud Computing and Social Media for Prostate Cancer Advocacy in Nigeria
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".