Men on Blue: Knowledge, Belief, Fear, Perceived Attitude of Men to Prostate Cancer Screening and Awareness in Sub-Saharan Africa
Bibliographic record
Abstract
Background and context: In Nigeria, cancer leads to >72,000 deaths per annum (30,924 for male and 40,647 for female). This number is set to increase given that there are 102,000 new cases of cancer every year. The estimated incidence for prostate cancer is (12%) and estimated mortality prostate (13%). Prostate cancer is the third leading cancer death in Nigeria and the leading cause of cancer deaths in Nigerian men. However, very little or nothing is said about prostate cancer in Nigeria. Every October, virtually all cancer NGOs roll out their drums of awareness focused on breast cancer, prostate cancer is always missing, while several men die in silence and pain because their prostate cancer was discovered at late stages. Men on Blue is a health intervention focused on closing the gap of awareness, education and screenings for prostate cancer in rural communities of Lagos, Abuja and Enugu. The intervention will use 3 core strategies, such as: prostate cancer awareness, prostate cancer screenings and social media campaigns. Our target is to screen 2000 men, reach 20,000 men directly, reach 30,000 women and youth directly in rural communities of Lagos, Abuja and Enugu and 5 million indirectly through traditional and social media in Nigeria. Aim: To reduce the incidence of prostate cancer through the creation of a platform for prostate cancer awareness, screening and support in Nigeria. Program/Policy process: The program use focused on phasing out late diagnosis of prostate cancer through screenings outreaches in local communities in Nigeria. Men are always missing in cancer awareness and programs, hence, the program will bring men to the fore of cancer awareness. Outcomes: It is expected that this program will increase the level of prostate cancer awareness in Nigeria through the translation of information materials in local languages, engage men to lead the campaign and the use of strategic social media campaign. What was learned: Preliminary results of the planning process of the program, showed that several men are battling with prostate cancer, however, very few is said about them and they are dying in silence. Their voice need to be heard in sub-Saharan Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".