Do Playful Parenting Programs Implemented at Scale Improve Caregiver Practices and Child Development?
Bibliographic record
Abstract
Background/Objectives: As an independent research group, we examined parent and child outcomes of three different parenting programs delivered at scale. The programs were implemented in Bhutan, Serbia and Zambia by different organizations. Methods: Mixed methods included a caregiver interview using the HOME Inventory, a direct child assessment using the Global Scales of Early Development (GSED) and focus group discussions with caregivers (FGD). Sampled mothers and children were randomly selected for the HOME/GSED: Bhutan n = 432, Serbia n = 636, Zambia n = 1024. Over 40 mothers and fathers of children under 3 years were purposively selected for FGD. Intention-to-treat and secondary regression analyses of attendees and non-attendees were conducted on the HOME and GSED; FGDs were subject to content analysis. Results: Parenting practices were found to be minimally (Bhutan) or modestly (Zambia) higher for caregivers who attended group sessions. Caregivers in Serbia who recalled receiving play messages had higher HOME scores. Child outcomes showed small (Bhutan) or no differences (Serbia, Zambia) associated with participation. Conclusions: Explanations focused on limits to program participation in scaled programs, the need for pilot evaluations to ensure that the program design is effective, and the need to monitor delivery quality and other implementation processes.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".