Factors influencing variation in implementation outcomes of the redesigned community health fund in the Dodoma region of Tanzania: a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Micro-health insurance (MHI) has been identified as a possible interim solution to foster progress towards Universal Health Coverage (UHC) in low- and middle- income countries (LMICs). Still, MHI schemes suffer from chronically low penetration rates, especially in sub-Saharan Africa. Initiatives to promote and sustain enrolment have yielded limited effect, yet little effort has been channelled towards understanding how such initiatives are implemented. We aimed to fill this gap in knowledge by examining heterogeneity in implementation outcomes and their moderating factors within the context of the Redesigned Community Health Fund in the Dodoma region in Tanzania. METHODS: We adopted a mixed-methods design to examine implementation outcomes, defined as adoption and fidelity of implementation (FOI) as well as their moderating factors. A survey questionnaire collected individual level data and a document review checklist and in-depth interview guide collected district level data. We relied on descriptive statistics, a chi square test and thematic analysis to analyse our data. RESULTS: A review of district level data revealed high adoption (78%) and FOI (77%) supported also by qualitative interviews. In contrast, survey participants reported relatively low adoption (55%) and FOI (58%). Heterogeneity in adoption and FOI was observed across the districts and was attributed to organisational weakness or strengths, communication and facilitation strategies, resource availability (fiscal capacity, human resources and materials), reward systems, the number of stakeholders, leadership engagement, and implementer's skills. At an individual level, heterogeneity in adoption and FOI of scheme components was explained by the survey participant's level of education, occupation, years of stay in the district and duration of working in the scheme. For example, the adoption of job description was statistically associated with occupation (p = 0.001) and wworking in the scheme for more than 20 months had marginal significant association with FOI (p = 0.04). CONCLUSION: The study demonstrates that assessing the implementation processes helps to detect implementation weaknesses and therefore address such weaknesses as the interventions are implemented or rolled out to other settings. Attention to contextual and individual implementer elements should be paid in advance to adjust implementation strategies and ensure greater adoption and fidelity of implementation.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".