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Record W4386161498 · doi:10.1080/14635240.2023.2248606

Kenyan youth’s understanding of cancer and cancer risk: a qualitative study

2023· article· en· W4386161498 on OpenAlexaff
David Busolo, Roberta L. Woodgate

Bibliographic record

VenueInternational Journal of Health Promotion and Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of ManitobaUniversity of New Brunswick
Fundersnot available
KeywordsConceptualizationKenyaQualitative researchCancerFocus groupPopulationEnvironmental healthMedicineSociologyPolitical scienceSocial scienceInternal medicine

Abstract

fetched live from OpenAlex

Cancer incidence and mortality continues to rise worldwide including in Kenya and Kenyan youth who take part in cancer risk behaviour increase their chances of getting the disease in future. Despite this awareness, little is known about how Kenyan youth conceptualize cancer and cancer risk. Therefore, using a qualitative research study approach, we explored Kenyan youth’s understanding of cancer and cancer risk. Fifty-three youth between ages 12–19 years were engaged through individual interviews and focus group discussions. Using qualitative analysis approaches, we arrived at youth’s conceptualization of cancer and cancer risk. In youth’s conceptualization of cancer, their descriptions are grouped into two themes: ‘Cancer as a mysterious illness’ and ‘Lay representations using metaphors’. In their conceptualization of cancer risk, youth described cancer risk in ways that are grouped as ‘Cancer risk as lifestyle factors’ and ‘The process of understanding risk’. Our study is the first of its kind to be conducted in Kenya and we present critical findings and recommendations for cancer and cancer risk awareness, health education, and promotion for this population.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.698
GPT teacher head0.630
Teacher spread0.068 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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