Developing and Implementing a Colorectal Cancer Screening Program in Uganda: Stakeholder Perceived Barriers and Opportunities
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) incidence is increasing in Uganda. Despite this, and the disproportionately high burden of early onset and late-stage CRC cases, no CRC screening program exists in Uganda. To guide and inform future CRC prevention efforts, interviews with key stakeholders were undertaken to better understand the perceived barriers and opportunities relevant to the development and implementation of a CRC screening program in Uganda. METHODS: Semi-structured key informant interviews were conducted with key stakeholders in cancer prevention, screening and policy/programming (n = 11, 6 medically qualified and 5 non-medical), who were recruited across Uganda using maximum variation sampling between March and April 2022. Interviews were audio recorded, transcribed, coded and later analysed using a deductive thematic analysis approach guided by the social ecological model. RESULTS: Major barriers included lack of government priority for CRC prevention programs, lack of resources/funding for CRC screening (policy level), inadequate screening facilities and equipment, limited training/knowledge of CRC and capacity of the health workforce (health system level), challenges in the delivery of CRC awareness messages (community level), emotions associated with CRC screening and poor awareness of CRC and its symptoms (individual level). Major opportunities included the existence of a draft national cancer control plan (policy level), existence of less costly CRC screening alternatives, less costly primary prevention measures (health system level), existence of community leadership and structures (community level), likely acceptability of the faecal occult blood test and peer support (individual level). CONCLUSION: There are substantive barriers to CRC screening program development and implementation in Uganda. However, there are signs, like the development of a cancer control plan, that suggest a shift towards strategic planning and allocation of resources at a population level for addressing the issues of cancer prevention and care, including CRC. In the meantime, efforts should prioritise primary prevention interventions such as mass education to promote CRC awareness.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".