Rater training for standardised assessment of Objective Structured Clinical Examinations in rural Tanzania
Bibliographic record
Abstract
OBJECTIVES: To describe a simulation-based rater training curriculum for Objective Structured Clinical Examinations (OSCEs) for clinician-based training for frontline staff caring for mothers and babies in rural Tanzania. BACKGROUND: Rater training for OSCE evaluation is widely embraced in high-income countries but not well described in low-income and middle-income countries. Helping Babies Breathe, Essential Care for Every Baby and Bleeding after Birth are standardised training programmes that encourage OSCE evaluations. Studies examining the reliability of assessments are rare. METHODS: Training of raters occurred over 3 days. Raters scored selected OSCEs role-played using standardised learners and low-fidelity mannikins, assigning proficiency levels a priori. Researchers used Zabar's criteria to critique rater agreement and mitigate measurement error during score review. Descriptive statistics, Fleiss' kappa and field notes were used to describe results. RESULTS: Six healthcare providers scored 42 training scenarios. There was moderate rater agreement across all OSCEs (κ=0.508). Kappa values increased with Helping Babies Breathe (κ=0.28-0.48) and Essential Care for Every Baby (κ=0.42-0.77) by day 3 of training, but not with Bleeding after Birth (κ=0.58-0.33). Raters identified average proficiency 50% of the time. CONCLUSION: Our study shows that the in-country raters in this study had a hard time identifying average performance despite moderate rater agreement. Rater training is critical to ensure that the potential of training programmes translates to improved outcomes for mothers and babies; more research into the concepts and training for discernment of competence in this setting is necessary.
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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 | 0.086 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".