Measuring implementation progress in kangaroo mother care
Bibliographic record
Abstract
AIM: To describe the development and testing of a monitoring model with quantitative indicators or progress markers that could measure the progress of individual hospitals in the implementation of kangaroo mother care (KMC). METHODS: Three qualitative data sets in the larger research programme on the implementation of KMC of the MRC Research Unit for Maternal and Infant Health Care Strategies in South Africa were used to develop a progress-monitoring model and an accompanying instrument. RESULTS: The model was conceptualized around three phases (pre-implementation, implementation and institutionalization) and six constructs depicting progress (awareness, adopting the concept, mobilization of resources, evidence of practice, evidence of routine and integration, sustainable practice). For each construct, indicators were developed for which data could be collected by means of the monitoring instrument used in a walk-through visit to a hospital. The instrument has been tested in 65 hospitals. CONCLUSION: The progress-monitoring model enables the quantification of individual hospitals' progress in the process of implementing KMC and an objective measurement of the effectiveness of different outreach strategies. The model also has potential to be adapted for measuring progress in other innovative healthcare interventions on a large scale.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".