An ethnographic study of Kenyan adolescents’ understanding of cancer, cancer risk, and cancer prevention
Bibliographic record
Abstract
Cancer incidence and mortality continues to rise worldwide including in the country of Kenya. Burdened with infectious diseases, poverty, and lack of proper cancer preventive plans, the future of cancer care in Kenya is unknown. This is further exacerbated by the fact that Kenyan adolescents engage in smoking, unhealthy eating, physical inactivity, and alcohol intake that can increase their lifetime cancer risk. Despite this awareness, little is known about Kenyan adolescents’ understanding of cancer, cancer risk, and cancer prevention. Such awareness is needed to inform germane cancer prevention and health promotion initiatives. Accordingly, an ethnographic qualitative study was carried out to explore Kenyan adolescents’ understanding of cancer, cancer risk, and cancer prevention. This study took place at Nairobi Primary and OlKeri Mixed Secondary Schools in Kenya. Fifty-three Kenyan adolescents between ages 12 and 19 that were attending the participating schools took part. Participants were grouped as early (ages 12-14), middle (ages 15-17), and late adolescence (ages 18-19). Qualitative data was collected through individual open-ended interviews and focus group discussions. Data analysis occurred concurrently with data collection. Thematic and content analysis approaches were utilized. Ethical considerations were observed throughout the study. Study results generated three main findings about Kenyan adolescents’ conceptualization of cancer, cancer risk, and cancer prevention. In their conceptualization of cancer, adolescents 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, adolescents 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, adolescents 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. This study is the first of its kind to be conducted in Kenya. The study findings significantly add to the body of knowledge about understanding adolescents’ conceptualization of cancer, cancer risk, and cancer prevention. Additionally, the study results will create a platform for future cancer prevention research and health promotion programs in Kenya and other parts 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".