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
BACKGROUND: The Sustainable Development Goals (SDGs) highlight the importance of investments in early childhood care and education (ECCE) 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 7-month steps (33 clusters/step). The primary outcome is children's school readiness (indexed by the total score on the International Development and Early Learning Assessment (IDELA)). Secondary child outcomes are children's IDELA domain scores and executive functions. Data are collected in cross-sectional surveys of 1089 children (11 children/cluster from 99 clusters) aged 4.5-5.5 years at four timepoints (baseline and at the end of each step). Additionally, we will enroll three non-randomized 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 7 months thereafter to measure secondary outcomes of youth personal and professional development, depressive symptoms, and executive functions. A non-randomized school cohort of 330 LEAPS students (10 students/cluster from 33 clusters) will also 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 timepoints midway following rollout and at endline. A concurrent mixed-methods implementation evaluation will assess program fidelity and quality, and 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. 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 on December 5, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".