Implementation process and quality of a primary health care system improvement initiative in a decentralized context: <scp>A</scp> retrospective appraisal using the quality implementation framework
Bibliographic record
Abstract
BACKGROUND: Effective implementation processes are essential in achieving desired outcomes of health initiatives. Whereas many approaches to implementation may seem straightforward, careful advanced planning, multiple stakeholder involvements, and addressing other contextual constraints needed for quality implementation are complex. Consequently, there have been recent calls for more theory-informed implementation science in health systems strengthening. This study applies the quality implementation framework (QIF) developed by Meyers, Durlak, and Wandersman to identify and explain observed implementation gaps in a primary health care system improvement intervention in Nigeria. METHODS: We conducted a retrospective process appraisal by analyzing contents of 39 policy document and 15 key informant interviews. Using the QIF, we assessed challenges in the implementation processes and quality of an improvement model across the tiers of Nigeria's decentralized health system. RESULTS: Significant process gaps were identified that may have affected subnational implementation quality. Key challenges observed include inadequate stakeholder engagements and poor fidelity to planned implementation processes. Although needs and fit assessments, organizational capacity building, and development of implementation plans at national level were relatively well carried out, these were not effective in ensuring quality and sustainability at the subnational level. CONCLUSIONS: Implementing initiatives between levels of governance is more complex than within a tier. Adequate preintervention planning, understanding, and engaging the various interests across the governance spectrum are key to improving quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".