Using normalisation process theory (NPT) to explore implementation of the maternal perinatal death surveillance and response (MPDSR) policy in Uganda: a reflection
Bibliographic record
Abstract
BACKGROUND: The implementation of the maternal perinatal death surveillance and response (MPDSR) policy is among the envisaged strategies to reduce the high global burden of maternal and perinatal mortality and morbidity. However, implementation of this policy across various contexts is inconsistent. Theoretically informed approaches to process evaluation can support assessment the implementation of policy interventions such as MPDSR, particularly in understanding what the actors involved actually do. In this article, we reflect on how the normalisation process theory (NPT) was used to explore implementation of the MPDSR policy in Uganda. NPT is a sociological theory concerned with the social organisation of the work (implementation) of making practices routine elements of everyday life (embedding) and of sustaining embedded practices in their social contexts (integration). METHODS: This qualitative multiple case study conducted across eight districts in Uganda and among 10 health facilities (cases) representing four out of the seven levels of the Uganda health care system. NPT was utilised in several ways including informing the study design, structuring the data collection tools (semi-structured interview guides), providing an organising framework for analysis, interpreting and reporting of study findings as well as making recommendations. Study participants were purposely selected to reflect the range of actors involved in the policy implementation process. This included direct care providers located at each of the cases, the Ministry of Health and from agencies and professional associations. Data were collected using semi-structured, in-depth interviews and were inductively and deductively analysed using NPT constructs and subconstructs. RESULTS AND CONCLUSION: NPT served useful for process evaluation, particularly in identifying factors that contribute to variations in policy implementation. Considering the NPT focus on the agency of people involved in implementation, additional efforts are required to understand how recipients of the policy intervention influence how the intervention becomes embedded within the various contexts.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".