Evaluating implementation of LEAPS, a youth-led early childhood care and education intervention in rural Pakistan: protocol for a stepped-wedge cluster randomized trial
Bibliographic record
Abstract
Abstract Background The Sustainable Development Goals (SDGs) highlight the importance of investments in early childhood care and education (ECCE) for young children and youth development. Given Pakistan’s large young population, and gender and urban-rural inequalities in access to education, training and employment, such investments offer opportunities. LEAPS is a youth-led ECCE program that trains female youth, 18-24 years, as Community Youth Leaders (CYLs) to deliver high-quality ECCE for children, 3.5-5.5 years, in rural Sindh, Pakistan. Methods We use a stepped-wedge cluster randomized trial to evaluate implementation of LEAPS. Ninety-nine clusters will be randomized to receive the intervention in one of three seven-month steps (33 clusters/step). Primary outcomes are children’s school readiness (measured with the International Development and Early Learning Assessment) and executive functions. Secondary outcomes are youth personal and professional development, depressive symptoms, and executive functions. Data is collected in cross-sectional surveys of 1,089 children (11 children/cluster from 99 clusters) aged 4.5-5.5 years at four time points (baseline and at the end of each step). We will enroll three youth participant open cohorts, one per step (33 CYLs: 66 comparison youth per cohort; 99:198 in total). Youth cohorts will be assessed at enrollment and every six to seven months thereafter (i.e., once per consecutive Step). A school cohort of 330 LEAPS students (10 students/cluster from 33 clusters) will be enrolled and assessed during Step 1 after intervention rollout and at endline. The quality of the learning environment will be assessed in each LEAPS ECCE center and in a comparison center at two time points midway following rollout and at endline. A concurrent mixed-methods implementation evaluation will assess program fidelity and quality, as well as the extent to which a technical support strategy is successful in strengthening systems for program expansion. A cost evaluation will assess cost-per-beneficiary. Data collection for implementation and cost evaluations will occur in Step 3. Discussion Youth-led models for ECCE offer a promising approach to support young children and youth; however, there is little empirical evidence on real-world implementation. This study will contribute to the evidence as a means to promote sustainable human development across multiple SDG targets. Trial Registration ClinicalTrials.gov: NCT03764436 . Registered December 5 th , 2018, https://clinicaltrials.gov/ct2/show/NCT03764436 .
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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 | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".