Research priority setting for integrated early child development and violence prevention (ECD+) in low and middle income countries: An expert opinion exercise
Bibliographic record
Abstract
Child development in low and middle income countries (LMIC) is compromised by multiple risk factors. Reducing children's exposure to harmful events is essential for early childhood development (ECD). In particular, preventing violence against children - a highly prevalent risk factor that negatively affects optimal child development - should be an intervention priority. We used the Child Health and Nutrition Initiative (CHNRI) method for the setting of research priorities in integrated Early Childhood Development and violence prevention programs (ECD+). An expert group was identified and invited to systematically list and score research questions. A total of 186 stakeholders were asked to contribute five research questions each, and contributions were received from 81 respondents. These were subsequently evaluated using a set of five criteria: answerability; effectiveness; feasibility and/or affordability; applicability and impact; and equity. Of the 400 questions generated, a composite group of 50 were scored by 55 respondents. The highest scoring research questions related to the training of Community Health Workers (CHW's) to deliver ECD+ interventions effectively and whether ECD+ interventions could be integrated within existing delivery platforms such as HIV, nutrition or mental health platforms. The priority research questions can direct new research initiatives, mainly in focusing on the effectiveness of an ECD+ approach, as well as on service delivery questions. To the best of our knowledge, this is the first systematic exercise of its kind in the field of ECD+. The findings from this research priority setting exercise can help guide donors and other development actors towards funding priorities for important future research related to ECD and violence prevention.
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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 | 0.135 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| 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".