Training early childhood development cadres in low-resource contexts
Bibliographic record
Abstract
This brief summarises findings from an extended literature review on the current status of early childhood development (ECD) cadres training and a Delphi survey of expert consensus on training needs for different ECD cadres operating in low-resource contexts (Pearson et al., 2017) titled Reaching expert consensus on training different cadres in delivering early childhood development at scale in low-resource contexts. The work was funded by DFID and led by a team at Bishop Grosseteste University in collaboration with colleagues from The University of Hong Kong, McGill University, University of Nebraska, University of Wollongong and University College London. The following overarching questions guided this study: ? To whom does the term ?ECD cadre? most usefully apply, given the wide range of settings and aims of early childhood development programmes? ? What are expert views on essential knowledge and skills required of ECD cadres working in different contexts? ? What are expert views on appropriate methods for delivery of training, and post-training follow-up, for ECD cadres? ? What are expert views on the necessary conditions for effective scale-up of ECD cadres training?
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".