Training early childhood development cadres in low-resource contexts. UK Government Department for International Development.
Bibliographic record
Abstract
This brief summarises findings from an extended literature review on the current status of early childhood\ndevelopment (ECD) cadres training and a Delphi survey of expert consensus on training needs for different\nECD cadres operating in low-resource contexts (Pearson et al., 2017) titled Reaching expert consensus on\ntraining different cadres in delivering early childhood development at scale in low-resource contexts. The\nwork was funded by DFID and led by a team at Bishop Grosseteste University in collaboration with\ncolleagues from The University of Hong Kong, McGill University, University of Nebraska, University of\nWollongong and University College London.\nThe following overarching questions guided this study:\n• To whom does the term ‘ECD cadre’ most usefully apply, given the wide range of settings and aims of\nearly childhood development programmes?\n• What are expert views on essential knowledge and skills required of ECD cadres working in different\ncontexts?\n• What are expert views on appropriate methods for delivery of training, and post-training follow-up, for\nECD cadres?\n• What are expert views on the necessary conditions for effective scale-up of ECD cadres training?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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".