Implementation of Clinical Practice Guidelines to Prevent Cervical Cancer: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is a common cause of death among women globally, particularly in Africa. Each year, an average of 7093 women in Nigeria die from cervical cancer. Clinical practice guidelines developed by the Society of Obstetrics and Gynecology of Nigeria (SOGON) aim to prevent cervical cancer. However, the extent of their adoption among gynecologists remains unclear. OBJECTIVE: This study aimed to assess Nigerian gynecologists' awareness, understanding, and incorporation of the SOGON clinical practice guidelines for cervical cancer prevention in their clinical practices. METHODS: A convergent parallel mixed methods design was used. Quantitative data were collected via a web-based and in-person survey distributed to gynecologists attending the 57th SOGON Annual General Meeting in Kano, Nigeria (November 2023). A total of 105 gynecologists completed the survey (response rate: 80%). Key informant interviews (n=12) were conducted to provide qualitative insights. Quantitative data were analyzed using descriptive and inferential statistics, including logistic regression (P<.05). Thematic analysis was applied to qualitative data. RESULTS: Among the 105 respondents (mean age 50, SD 8.3 y and mean postresidency practice 12, SD 9.4 y), 98 (93.3%) reported awareness of the SOGON guidelines, and 74 (70.5%) endorsed their importance for cervical cancer prevention. However, only 58.1% (61/105) of the respondents reported integrating the guidelines into routine clinical practice. Barriers to implementation included limited training (71/105, 67.6%), resource constraints (64/105, 60.9%), and lack of institutional support (57/105, 54.3%). Qualitative data reinforced the need for more tailored guidelines for high-risk populations and rural settings. In addition, 70.5% (74/105) of the respondents advocated for a participatory guideline review process to ensure relevance and feasibility. CONCLUSIONS: While awareness of the SOGON guidelines is high, their integration into clinical practice remains suboptimal due to systemic barriers. Strengthening training programs, improving access to resources, and enhancing institutional support are critical to increasing guideline adoption and advancing cervical cancer prevention efforts in Nigeria.
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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 | 0.049 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".