Using Implementation Research to Inform Scaling of Parenting Programs: Independently Conducted Case Studies from Zambia and Bhutan
Bibliographic record
Abstract
Two case studies of parenting programs for parents of children 0 to 36 months of age, developed and implemented by Save the Children/Ministry of Health/Khesar Gyalpo University in Bhutan and UNICEF Zambia, were conducted by an independent research group. The focus was on how program delivery and scale-up were revised on the basis of feedback from implementation research. Feedback on workforce delivery quality was based on observations of deliveries using a monitoring form, as well as survey and interview data collected from the workforce. In-depth interviews with the resource team during the fourth year of implementation revealed how the feedback was used to address horizontal and vertical scaling. Delivery quality was improved in some cases by revising the delivery manual, offering refresher courses, and instituting regular monitoring. Scaling challenges in Zambia included slow progress with regard to reaching families in the two districts, which they addressed by trialing group sessions, and stemming workforce attrition. The challenges in Bhutan were low attendance and reducing the workload of providers. Vertical scaling challenges for both countries concerned maintaining demand through continuous advocacy at community and government levels to sustain financing and to show effectiveness in outcomes.
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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 | 0.049 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".