A process evaluation plan for assessing a complex community-based maternal health intervention in Ogun State, Nigeria
Bibliographic record
Abstract
BACKGROUND: Despite increased investment in community-level maternal health interventions, process evaluations of such interventions are uncommon, and can be instrumental in understanding mediating factors leading to outcomes. In Nigeria, where an unacceptably number of maternal deaths occur (maternal mortality ratio of 814/100,000 livebirths), the Community Level Interventions for Pre-eclampsia (CLIP) study (NCT01911494) aimed to reduce maternal and neonatal mortality and morbidity with a complex intervention of five interrelated components. Building from previous frameworks, we illustrate a methodology to evaluate implementation processes of the complex CLIP intervention, assess mechanisms of impact and identify emerging unintended causal pathways. METHODS: The study was conducted from 2013-2016 in five Local Government Areas in Ogun State, Nigeria. A six-step approach was developed to evaluate key constructs of context (external factors related to intervention), implementation (fidelity, dose, reach, and adaption) and mechanisms of impact (unintended outcomes and mediating pathways). The steps are: 1) describing the intervention by a logic model, 2) defining acceptable delivery, 3) formulating questions, 4) determining methodology, 5) planning resources in context, lastly, step 6) finalising the plan in consideration with relevant stakeholders. RESULTS: Quantitative data were collected from 32,785 antenatal and postnatal visits at the primary health care level, from 66 community engagement sessions, training assessments of community health workers, and standard health facility questionnaires. Forty-three focus group discussions, 38 in-depth interviews, and 23 structured observations were conducted to capture qualitative data. A total of 103 community engagement reports and 182 suspected pre-eclampsia case reports were purposively collected. Timing of data collection was staggered to understand feedback mechanisms that may have resulted from the delivery of the intervention. Data will be analysed using R and NVivo. Diffusions of innovations and realist evaluation theories will underpin analysis of the interaction between context, mechanisms and outcomes. CONCLUSION: This comprehensive approach can serve as a guide for researchers and policy makers to plan the evaluation of similar complex health interventions in resource-constrained settings, and to aid in measuring 'effectiveness' of interventions and not just 'efficacy'. TRIAL REGISTRATION: This research is a part of the Community Level Interventions for Pre-eclampsia Study, NCT01911494. The trial is registered in Clinicaltrials.gov, the URL is https://clinicaltrials.gov/ct2/show/NCT01911494 The trial was registered on June 28, 2013 and the first participant was enrolled for intervention on March 1, 2014.
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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 | 0.157 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".