A Tale of Two Programs for Parents of Young Children: Independently-Conducted Case Studies of Workforce Contributions to Scale in Bhutan and Rwanda
Bibliographic record
Abstract
Two case studies of parenting programs, aiming to improve parenting practices and child development outcomes, and implemented by Save the Children/Ministry of Health/Khesar Gyalpo University in Bhutan and Boston College/University of Rwanda/FXB in Rwanda, respectively called Prescription to Play and Sugira Muryango, were conducted by an independent research and learning group. Implementation research focused on the workforce, a crucial but little-studied element determining the success of programs going to scale. Mixed methods were used to examine their training, workload, challenges, and quality of delivery. Health assistants in Bhutan and volunteers in Rwanda were trained for 10-11 days using demonstrations, role plays, and manuals outlining activities to deliver to groups of parents (Bhutan) or during home visits (Rwanda). Workers' own assessments of their delivery quality, their confidence, and their motivations revealed that duty, confidence, and community respect were strong motivators. According to independent observations, the quality of their delivery was generally good, with an overall mean rating on 10 items of 2.36 (Bhutan) and 2.44 (Rwanda) out of 3. The facilitators of scaling for Bhutan included institutionalizing training and a knowledgeable workforce; the barrier was an overworked workforce. The facilitators of scaling for Rwanda included strong follow-up supervision; the barriers included high attrition among a volunteer workforce.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".