Improving health worker motivation and performance to deliver adolescent sexual and reproductive health services in the Democratic Republic of Congo: study design of implementation research to assess the feasibility, acceptability, and effectiveness of a package of interventions
Bibliographic record
Abstract
During its last funding cycle from 2018-2020, the Global Fund in collaboration with the Ministry of Health, World Health Organization, and implementing partners Cordaid and Santé Rural (SANRU), implemented a multi-sectoral, contextualized approach to improve the sexual and reproductive health of adolescent girls and young women in two regions in the Democratic Republic of the Congo, which included community-based, school-based and health facility-based actions. This implementation research focuses on the health-facility component. The objective of this research is to evaluate the feasibility, acceptability, and effectiveness of a package of interventions to improve health workers' knowledge, skills, and attitudes in providing sexual and reproductive health services to adolescents, whilst concomitantly creating an enabling work environment for building health workers' motivation. The package includes a combination of job descriptions, training and refresher training, desk reference tools, and collaborative learning. The package did not focus on improving amenities, providing or repairing equipment, or providing medicines and supplies. The underlying theoretical framework informing the project and the implementation research draws from Social Network Theory, Diffusion of Innovations and Normalization Process Theory. Qualitative and quantitative process and outcome data from in-depth interviews and focus group discussions with health workers and health managers, field notes, monitoring reports, costing sheets, and health worker surveys, adolescent mystery client assessments, and exit interviews with adolescents will be collected as part of a time-series study. The findings from this implementation research will be utilized to inform future adaptations and/or scale-up of the package of interventions to improve health worker motivation and performance in the Democratic Republic of the Congo and elsewhere. The findings will also contribute to advancing the use of theoretical approaches within the field of implementation research.
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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 | 0.043 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".