Health service factors influencing uptake of cervical visual inspection with acetic acid in selected health facilities in Embu County, Kenya
Bibliographic record
Abstract
BACKGROUND Cervical cancer is the second most common cancer among females in developing countries. Despite widespread screening efforts for cancer of the cervix and training on VIA/VILLI, deaths due to cervical cancer remain high. This study aimed to determine Health Service Provider factors influencing the uptake of cervical cancer screening by VIA in selected health facilities in Embu County, Kenya. METHODOLOGY Data were collected from 14 healthcare providers who were the initial study respondents from 7 purposively selected health facilities. Data collection tools were self-administered questionnaires and structured interviews for key informants. Additional secondary data were obtained from the health facility records and KDHS 2014. For data analysis, both quantitative and qualitative techniques of analysis were applied. RESULTS Lack of awareness creation on VIA, lack of skills to do VIA, lack of supplies for VIA, cost and fear of speculum examination by women were some factors leading to low uptake of cervical cancer screening. CONCLUSIONS AND RECOMMENDATIONS Increasing awareness of VIA needs to be done such as through recruitment of male champions for cervical cancer screening to mobilize and participate in awareness campaigns. Moreover, there needs to be an increase in the number of health care providers trained in VIA and cryotherapy and creating a pool of trainers of other health service providers. Additionally, the provision of VIA supplies needs to be ensured and VIA services offered free of charge. MOH and county governments' departments of health should ensure support supervision at least once per quarter to the health care workers trained on VIA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".