Global research priorities to accelerate programming to improve early childhood development in the sustainable development era: a CHNRI exercise
Bibliographic record
Abstract
BACKGROUND: Approximately 250 million children under the age of five in low and middle-income countries (LMICs) will not achieve their developmental potential due to poverty and stunting alone. Investments in programming to improve early childhood development (ECD) have the potential to disrupt the cycle of poverty and therefore should be prioritised. Support for ECD has increased in recent years. Nevertheless, donors and policies continue to neglect ECD, in part from lack of evidence to guide policy makers and donors about where they should focus policies and programmes. Identification and investment in research is needed to overcome these constraints and in order to achieve high quality implementation of programmes to improve ECD. METHODS: The Child Health and Nutrition Research Initiative (CHNRI) priority setting methodology was applied in order to assess research priorities for improving ECD. A group of 348 global and local experts in ECD-related research were identified and invited to generate research questions. This resulted in 406 research questions which were categorised and refined by study investigators into 54 research questions across six thematic goals which were evaluated using five criteria: answerability, effectiveness, feasibility, impact, and effect on equity. Research options were ranked by their final research priority score multiplied by 100. RESULTS: The top three research priority options from the LMIC experts came from the third thematic goal of improving the impact of interventions, whereas the top three research priority options from high-income country experts came from different goals: improving the integration of interventions, increasing the understanding of health economics and social protection strategies, and improving the impact of interventions. CONCLUSION: The results of this process highlight that priorities for future research should focus on the need for services and support to parents to provide nurturing care, and the training of health workers and non-specialists in implementation of interventions to improve ECD. Three of the six thematic goals of the present priority setting centred on interventions (ie, improving impact, implementation of interventions and improving the integration of interventions). In order to achieve higher coverage through sustainable interventions to improve ECD with equitable reach, interventions should be integrated and not be sector driven.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".