Human resources and curricula content for early child development implementation: multicountry mixed methods evaluation
Bibliographic record
Abstract
OBJECTIVE: The WHO recommends responsive caregiving and early learning (RCEL) interventions to improve early child development (ECD), and to achieve the Sustainable Development Goals' vision of a world where all children thrive. Implementation of RCEL programmes in low and middle-income countries (LMIC) requires evidence to inform decisions about human resources and curricula content. We aimed to describe human resources and curricula content for implementation of RCEL projects across diverse LMICs, using data from the Grand Challenges Canada Saving Brains ECD portfolio. SETTING: We evaluated 32 RCEL projects across 17 LMICs on four continents. PARTICIPANTS: Overall, 2165 workers delivered ECD interventions to 25 909 families. INTERVENTION: Projects were either stand-alone RCEL or RCEL combined with health and nutrition, and/or safety and security. PRIMARY AND SECONDARY OUTCOMES: We undertook a mixed methods evaluation of RCEL projects within the Saving Brains portfolio. Quantitative data were collected through standardised reporting tools. Qualitative data were collected from ECD experts and stakeholders and analysed using thematic content analysis, informed by literature review. RESULTS: Major themes regarding human resources included: worker characteristics, incentivisation, retention, training and supervision, and regarding curricula content: flexible adaptation of content and delivery, fidelity, and intervention duration and dosage. Lack of an agreed standard ECD package contributed to project heterogeneity. Incorporation of ECD into existing services may facilitate scale-up but overburdened workers plus potential reductions in service quality remain challenging. Supportive training and supervision, inducement, worker retention, dosage and delivery modality emerged as key implementation decisions. CONCLUSIONS: This mixed methods evaluation of a multicountry ECD portfolio identified themes for consideration by policymakers and programme leaders relevant to RCEL implementation in diverse LMICs. Larger studies, which also examine impact, including high-quality process and costing evaluations with comparable data, are required to further inform decisions for implementation of RCEL projects at national and regional scales.
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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 | 0.114 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".