Knowledge Attitude and Practices of Health Care Workers towards Colorectal Cancer Screening in Primary Care Settings in Durban, South Africa: A Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: South Africa has the highest number of colorectal cancer (CRC) patients in sub-Saharan Africa, with the CRC projected new cases at 8 000 per 100 000 population by 2030. Screening assists with the early detection and control of cancer. This study determined knowledge, attitude, and practices (KAP) related to CRC among Health Care Workers (HCWs). METHODS: A cross-sectional descriptive study was conducted between April and November 2021 using a self-administered close-ended questionnaire. Data was collected from 109 HCWs in public primary health care facilities in Durban, South Africa. Summary descriptive and association analysis were conducted using IBM SPSS vs. 28. RESULTS: Overall CRC screening knowledge score was 12% (mean 13) with 39% that were familiar with the National Department of Health Cancer Control framework. Only 15% of participants perceived the Perceived the National Cancer Control Guidelines to be influential for the implementation of colorectal cancer screening. 70% of participants would recommend CRC screening to patients. Over one-fifth, (22%) of participants felt that fecal occult blood, flexible sigmoidoscopy, and colonoscopy were effective for CRC screening. Over a third (44%) preferred a structured CRC screening programme. Most participants (81%) were willing to recommend CRC screening to their patients. Only 10% of participants had ever conducted colorectal screening before. The vast majority were unfamiliar with the types of CRC screening tests. Lack of CRC screening guidelines, training, equipment and CRC low burden were identified as barriers to screening. CONCLUSION: The vast majority of HCWs lacked knowledge of the CRC screening programme and its procedures. However, the vast majority of HCWs were willing to conduct screening once trained. This study also highlighted perceived health systems barriers affecting CRC screening. Currently, South Africa does not have national guidelines for CRC screening in South Africa, hence, a national risk differentiation CRC screening guideline is needed to guide implementation at the PHC level. Health systems strengthening interventions, including training of HCWs, availability of screening tests and materials to facilitate the integration of CRC screening noting that PH already implements screening programmes for other cancer types.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".