The development of proxy indicators to measure workforce well-being in emergency obstetric and neonatal care settings
Bibliographic record
Abstract
Background: A shortage of health workers with skills to deliver high-quality routine and emergency maternal and newborn health (MNH) care hinders achievement of the Sustainable Development Goals. Health worker well-being is a determinant of the quality and experiences of care. The emergency obstetric and newborn care (EmONC) framework supports country-level planners and managers to evaluate EmONC systems. We describe a measure development activity to identify a brief set of tracer measures to monitor facility-level health worker well-being in EmONC facilities. Methods: We did an iterative, systematic, rapid scoping review of the literature on health worker well-being constructs and existing measures, complemented by focus group discussions and expert technical consultations. We systematically mapped, evaluated, and prioritised candidate items for indicator development, drawing on the literature and inputs from content experts (MNH health workers and stakeholders) and measurement experts, to select succinct, relevant, actionable tracer measures for monitoring health worker well-being at the facility, sub-national, and national levels. We chose the final items for indicator development based on clarity, sensitivity to change, interpretability, usefulness, generalisability, and reliability. Results: Burnout, lack of psychological safety, and moral distress were prioritised to serve as proxies for the whole domain of EmONC health worker well-being. We compiled three brief measures fully reflecting the operational definition for each construct, including complete metadata. Next steps include collecting pilot data and performing psychometric testing to validate the measures. Subsequently, we hope they can be implemented as tracer indicators to monitor overall facility-based health worker well-being. Conclusions: We propose these candidate indicators as proxies to raise a 'red flag' when health worker well-being is low. We hope that they can alert health system administrators to the need to identify and address the root causes of threats to well-being, ensuring facility readiness to deliver EmONC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".