A process evaluation of integrated service delivery of self-collected HPV-based cervical cancer screening using RE-AIM in the ASPIRE Mayuge pragmatic randomized trial
Bibliographic record
Abstract
Background In many low-resourced settings, self-collected HPV-based cervical cancer screening (SCS) is being rolled out through task shifting to community health workers (CHWs). Process evaluations are needed to ensure SCS programs are effective and translate to community-based contexts. Methods The Advances in Screening and Prevention in Reproductive Cancers (ASPIRE) study in Mayuge, Uganda was a two-arm, pragmatic randomized trial comparing two SCS implementation strategies facilitated by CHWs: Door-to-door and Community health day recruitment. This adjunct study uses the RE-AIM evaluation framework to assess the Reach, Efficacy, Adoption, Implementation and Maintenance of each implementation strategy in a subpopulation using process data collected throughout the trial. Results Of the trial population (n=2019), 781 women participated in both the baseline and exit surveys (door-to-door: n=406; community health day: n=375) and are included in this analysis. Both implementation strategies demonstrated high Reach, Efficacy, Adoption, Implementation and Maintenance. Trial consent rate was high and 100% of consenting participants in both arms participated in SCS (Reach). Follow-up rates among HPV positive participants were also high in both arms (door-to-door: 84% and community health day: 74%) (Efficacy). The intervention employed 61 CHWs, 7 nurses, 3 health facilities and other local staff within the health system to implement the intervention (Adoption). The community health day arm received HPV screening results and visual inspection with acetic acid (VIA) quicker than the door-to-door arm, but reported higher dissatisfaction with wait times (Implementation). While women had knowledge of cervical cancer symptoms and prevention measures at six-months post-intervention, no one in either arm recalled that cervical cancer could be asymptomatic (Maintenance). Conclusion Both SCS implementation strategies performed well, demonstrating high Reach, Efficacy, Adoption, Implementation and Maintenance throughout participating communities. Implementing pragmatic approaches including task-shifting to CHWs can reduce health worker burden and improve screening access in low-resourced, community-based settings.
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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 | 0.060 | 0.078 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".