Self-Collection for HPV Testing: Potential Issues Related to Performance Measures and Quality Improvement Among Federally Qualified Health Centers
Bibliographic record
Abstract
CONTEXT: Most incident cases of cervical cancer in the United States are attributable to inadequate screening. Federally qualified health centers (FQHCs) serve a large proportion of women who are low-income, have no insurance, and are underserved-risk factors for insufficient cervical cancer screening. FQHCs must maintain quality measures to preserve their accreditation, address financial reimbursements, and provide quality care. Implementation of human papillomavirus (HPV) self-collection can improve cervical cancer screening coverage within FQHCs. OBJECTIVES: To understand perspectives from clinical personnel on current cervical cancer screening rates at FQHCs in North Carolina and the impact of implementing HPV self-collection among underscreened patients on screening rates and performance measures. DESIGN: The study used focus groups and key informant interviews. Coding-based thematic analysis was applied to both focus group and interview transcripts. Emergent themes regarding perspectives on self-collection implementation were mapped onto Consolidated Framework for Implementation Research (CFIR) constructs to identify future barriers and facilitators to implementation. SETTING: Two FQHCs in North Carolina and a cloud-based videoconferencing platform. PARTICIPANTS: Six FQHCs in North Carolina; 45 clinical and administrative staff from the 6 FQHCs; 1 chief executive officer (n = 6), 1 senior-level administrator (n = 6), 1 chief medical officer (n = 6), and 1 clinical data manager (n = 6) from each FQHC. MAIN OUTCOME MEASURE: Achievement of clinical perspectives. RESULTS: Societal-, practice-, and patient-level factors currently contribute to subpar cervical cancer screening rates. HPV self-collection was expected to improve screening uptake among underscreened women at FQHCs, and thus quality and performance measures, by offering an alternative screening approach for in-clinic or at-home use. Implementation barriers include financial uncertainties and HPV self-collection not yet a Food and Drug Administration-approved test. CONCLUSION: HPV self-collection has potential to improve cervical cancer screening quality and performance measures of FQHCs. For a successful implementation, multilevel factors that are currently affecting low screening uptake need to be addressed. Furthermore, the financial implications of implementation and approval of HPV self-collection as a test for cervical cancer screening quality measures need to be resolved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".