Setting global research priorities for private sector child health service delivery: Results from a CHNRI exercise
Bibliographic record
Abstract
BACKGROUND: The private health sector is an important source of sick child care, yet evidence gaps persist in best practices for integrated management of private sector child health services. Further, there is no prioritized research agenda to address these gaps. We used a Child Health and Nutrition Research Initiative (CHNRI) process to identify priority research questions in response to these evidence gaps. CHNRI is a consultative approach that entails prioritizing research questions by evaluating them against standardized criteria. METHODS: We engaged geographically and occupationally diverse experts in the private health sector and child health. Eighty-nine experts agreed to participate and provided 150 priority research questions. We consolidated submitted questions to reduce duplication into a final list of 50. We asked participants to complete an online survey to rank each question against 11 pre-determined criteria in four categories: (i) answerability, (ii) research feasibility, (iii) sustainability/equity, and (iv) importance/potential impact. Statistical data analysis was conducted in SAS 9.4 (SAS Institute Inc, Cary NC, USA). We weighted all 11 evaluation criteria equally to calculate the research priority score and average expert agreement for each question. We disaggregated results by location in high-income vs low- and middle-income countries. RESULTS: Forty-nine participants (55.1%) completed the online survey, including 33 high-income and 16 low- and middle-income country respondents. The top, prioritized research question asks whether accreditation or regulation of private clinical and non-clinical sources of care would improve integrated management of childhood illness services. Four of the top ten research priorities were related to adherence to case management protocols. Other top research priorities were related to training and supportive supervision, digital health, and infant and newborn care. Research priorities among high-income and low- and middle-income country respondents were highly correlated. CONCLUSION: To our knowledge, this is the first systematic exercise conducted to define research priorities for the management of childhood illness in the private sector. The research priorities put forth in this CHNRI exercise aim to stimulate interest from policy makers, program managers, researchers, and donors to respond to and help close evidence gaps hindering the acceleration of reductions in child mortality through private sector approaches.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".