A Process Evaluation to Assess Contextual Factors Associated With the Uptake of a Rapid Response Service to Support Health Systems’ Decision-Making in Uganda
Bibliographic record
Abstract
BACKGROUND: Although proven feasible, rapid response services (RRSs) to support urgent decision and policymaking are still a fairly new and innovative strategy in several health systems, more especially in low-income countries. There are several information gaps about these RRSs that exist including the factors that make them work in different contexts and in addition what affects their uptake by potential end users. METHODS: We used a case study employing process evaluation methods to determine what contextual factors affect the utilization of a RRS in Uganda. We held in-depth interviews with researchers, knowledge translation (KT) specialists and policy-makers from several research and policy-making institutions in Uganda's health sector. We analyzed the data using thematic analysis to develop categories and themes about activities and structures under given program components that affected uptake of the service. RESULTS: We identified several factors under three themes that have both overlapping relations and also reinforcing loops amplifying each other: Internal factors (those factors that were identified as over which the RRS had full [or almost full] control); external factors (factors over which the service had only partial influence, a second party holds part of this influence); and environmental factors (factors over which the service had no or only remote control if at all). Internal factors were the design of the service and resources available for it, while the external factors were the service's visibility, integrity and relationships. Environmental factors were political will and health system policy and decision-making infrastructure. CONCLUSION: For health systems practitioners considering RRSs, knowing what factors will affect uptake and therefore modifying them within their contexts is important to ensure efficient use and successful utilization of the mechanisms.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".