Enhancing Access to Family Planning Services in Uganda Through Community Health Extension Workers: Protocol for a Pilot Evaluation
Bibliographic record
Abstract
Background: In Uganda, 22% of all women of reproductive age have an unmet need for family planning services. Access to contraceptive services, especially long-term reversible contraceptives such as implants, remains a challenge. The number of trained health providers is also not sufficient to address the needs for contraception. The Uganda Ministry of Health implemented a community-based implant provision pilot project where community health extension workers (CHEWs) were trained and accredited to insert implants at community level. Objective: This study aims to evaluate the implementation and acceptability of stakeholders toward task shifting the provision of family planning implants to CHEWs in Uganda. Methods: The evaluation will use a cross-sectional design using both quantitative and qualitative methods. The quantitative component will use a noninferiority design, whereas the qualitative component will use a descriptive approach. The noninferiority design involves a comparison of the competence of the currently authorized cadre to offer the service to the proposed cadre (CHEWs). Compared with a randomized controlled trial, the noninferiority design is more appropriate for this evaluation because the CHEWs and the authorized cadre are not comparable in terms of level of training and competencies. The authorized cadre has gone through formal training, which is not comparable with the training the CHEWs have received, and so the comparison is such that the competencies of the CHEWs are noninferior or at most equal to the competencies of the authorized cadre. Quantitative data will be collected among 92 CHEWs and 92 qualified health workers using performance assessment checklists and practice-based questionnaires that were developed based on the training manuals. Competency will be measured on a continuous scale and summarized as mean (SD) scores. Qualitative data will be collected through key informant interviews (n=23), in-depth interviews (n=24), and focus group discussions (n=18). Qualitative data will be analyzed using thematic analysis following the framework method for the analysis of qualitative data using ATLAS.ti (version 9). Results: Preliminary findings indicate improved confidence and capacity of community health workers to provide implants despite challenges such as poor waste disposal, record keeping, and data management. By August 2025, training of research assistants had been concluded, and data collection had started. We anticipate that the data collection will be completed by the end of October 2025, the data analysis will be completed by November 2025, and the final results will be published by December 2026. Conclusions: This pilot will generate contextual information that can be used to improve access to family planning services at the community level.
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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 | 0.085 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".