Kenyan Youth Understanding of Cancer, Cancer Risk and Cancer Prevention: An Ethnographic Study
Bibliographic record
Abstract
Background: Cancer incidence and mortality continues to rise worldwide including in Kenya. Among the groups that are likely to get cancer in future are Kenyan youth that engage in behavior that can increase their lifetime cancer risk. Despite this awareness, little is known about Kenyan youth's understanding of cancer, cancer risk, and cancer prevention. Such awareness is needed to inform germane cancer prevention and health promotion initiatives. Aim: The purpose of this ethnographic study was to explore Kenyan youth's understanding of cancer, cancer risk, and cancer prevention. Methods: Fifty-three youth (ages 12-19) took part in individual interviews and focus group discussions. Results: In their conceptualization of cancer, youth described cancer in ways that are grouped into two themes: there is no other disease like it and lay understanding through metaphors. In their conceptualization of cancer risk, youth described cancer in ways that are grouped as cancer risk as lifestyle factors and the process of risk perception. Finally, in conceptualization of cancer prevention, youth described cancer prevention in ways that are grouped into the following themes: avoiding cancer risk factors, avoiding peers who partake in risk factors, and being healthy. Conclusion: This study is the first of its kind to be conducted in Kenya and adds to the body of knowledge in this area. Despite limited cancer control plans, youth described the grim consequences of getting cancer, their chances of getting the disease, and proposed opportunities for prevention. The study results will create a platform for future cancer prevention research and health promotion programs in Kenya and other part of 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 | 0.002 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".