Scaling parenting programs for early child development in four low- and middle-income countries
Bibliographic record
Abstract
Background Although meta-analyses have demonstrated the value of parenting programs to promote child development in low- and middle-income countries, scaling them horizontally and vertically through the system has remained largely undocumented. This study examines the enablers and barriers to scaling parenting programs implemented by different organizations in four countries, namely Bhutan, Rwanda, Serbia, and Zambia. Method An independent research and learning organization collected multi-method data from three sources, toward the end of a four-year period, to identify enablers and barriers of scale. The sources and method included: in-depth semi-structured interviews with two members of the technical resource teams ( n = 8); phone surveys with a random sample of providers who delivered the program to caregivers ( n = 529) along with in-depth interviews with a smaller number of providers ( n = 44); and in-depth semi-structured interviews with key government stakeholders ( n = 57). Content analysis was conducted to identify interviewees’ comments that reflected enablers and barriers to scale. Results Findings are presented to address horizontal and vertical enablers and barriers in each of the four country programs. Regarding horizontal scale, the main enabler was an existing workforce who was quickly trained to deliver the program and who perceived a need within their communities. Expanding the reach of the programs also required advocacy to raise demand among community leaders and caregivers. Design features of the programs, such as curriculum, modality, and dosage, contributed to effective outcomes as a function of their adaptation to providers’ and caregivers’ experiences. The main enabler of vertical scale was adoption by the government, integration into the system, and engagement of multisectoral stakeholders. Based on final reflections of stakeholders, qualitative data were provided for eight indicators of successful scale: demand, reach, equity, and workforce (for horizontal scale); multisectorality, adoption, policy/finance, and integration (for vertical scale). Conclusion Planning for scale needs to be done at the start by considering facilitative design features, selection of a workforce, and ownership by the government. Ongoing implementation research conducted with different stakeholders is needed to provide feedback for course-correction during the process of scale. Eight indicators can be used to evaluate the level of successful scale achieved by programs.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".