Workforce preparation for delivery of nurturing care in low‐ and middle‐income countries: Expert consensus on critical multisectoral training needs
Bibliographic record
Abstract
BACKGROUND: Services to support nurturing care through early childhood development (ECD) in low- and middle-income countries are hampered by significant workforce challenges. The global early childhood workforce is both diverse and complex, and it supports the delivery of a wide range of services in extremely diverse geographical and social settings. In the context of contemporary global goals for the universal provision of quality early childhood provision, there is an urgent need to build appropriate platforms for strengthening and supporting this workforce. However, the evidence base to support this work is severely limited. METHODS: To contribute to evidence on how to strengthen the ECD workforce in low- and middle-income countries, this study used a Delphi methodology involving three rounds of data collection with 14 global experts, to reach consensus on the most critical training needs of three key early childhood workforce groups: (i) health; (ii) community-based paraprofessionals, and (iii) educational professionals working across ECD programmes. RESULTS: The study identified a comprehensive set of shared, as well as distinct, training needs across the three groups. Shared training needs include the following: (i) nurturing dispositions that facilitate work with children and families in complex settings; (ii) knowledge and skills to support responsive, adaptable delivery of ECD programmes; and (iii) systems for ECD training and professional pathways that prioritise ongoing mentoring and support. CONCLUSIONS: The study's detailed findings help to address a critical gap in the evidence on training needs for ECD workers in low-resource contexts. They provide insights into how to strengthen content, systems, and methods of training to support intersectoral ECD work in resource-constrained contexts.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".