Transforming breast cancer control campaigns in low and middle-income settings: Tanzanian experience with ‘Check It, Beat It’
Bibliographic record
Abstract
Breast cancer incidence and mortality rates are similar in low resource settings like Tanzania. Structural and sociocultural barriers make late presentation typical in such settings where treatment options for advanced stage disease are limited. In the absence of national programmes, stand-alone screening campaigns tend to employ clinical models of delivery focused on individual behaviour and through a disease specific lens. This paper describes a case study of a 2010 stand-alone campaign in Tanzania to argue that exclusively clinical approaches can undermine screening efforts by premising that women will act outside their social and cultural domain when responding to screening services. A focus on sociocultural barriers dictated the approach and execution of the intervention. Our experience concurs with that in similar settings elsewhere, underscoring the importance of barriers situated within the sociocultural milieu of societies when considering prevention interventions. Culturally competent delivery could contribute to long-term reductions in late stage presentation and increases in treatment acceptance. We propose a paradigm shift in the approach to stand-alone prevention programmes.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".