The Perceptions of Professional Nurses Regarding the Performance of Cervical Cancer Screening, in Makhuduthamaga Sub-district, Sekhukhune District, Limpopo Province, South Africa
Bibliographic record
Abstract
Cervical cancer is regarded as the most common diagnosed type of cancer and resulting in high cancer related mortality amongst women. Cervical cancer related mortality rate is a serious challenge in Africa as compared to other countries, which needs a high collaborative approach amongst all health professionals. However, the higher incidence and death rate of cervical cancer implies that, high need of cervical cancer screening measures are necessary. A qualitative, descriptive approach was conducted through focus group discussions to establish perceptions of professional nurses regarding the provision of cervical cancer screening services in clinics. Challenges regarding the provision of cervical cancer services in clinics were raised by professional nurses during interviews. Verbatim data was collected by using interview guides and analyzed using Tesch’s 8-step approach in the coding process. Perceptions such as cultural beliefs, lack of resources for conduction of cervical cancer screening and transportation of Pap smear specimens and results, inadequate provision of information and pap smear results to clients were raised by professional nurses as contributing to cervical cancer uptake. Lack of standardized cervical cancer screening training thus leading to professional nurses not being sure of their performance regarding the provision of cervical cancer screening services were also stated by participants as challenges influencing uptake of cervical cancer screening. However, lack of standardized training about cervical cancer screening was found to be affecting the performance on provision of cervical screening services. A need for standardized cervical cancer screening training for all professional nurses is recommended.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".