Feasibility and preliminary validity evidence for remote video-based assessment of clinicians in a global health setting
Bibliographic record
Abstract
BACKGROUND: Serious childhood illnesses (SCI), defined as severe pneumonia, severe dehydration, sepsis, and severe malaria, remain major contributors to amenable child mortality worldwide. Inadequate recognition and treatment of SCI are factors that impact child mortality in Botswana. Skills assessments of providers caring for SCI have not been validated in low and middle-income countries. OBJECTIVE: To establish preliminary inter-rater reliability, validity evidence, and feasibility for an assessment of providers who care for SCI using simulated patients and remote video capture in community clinic settings in Botswana. METHODS: This was a pilot study. Four scenarios were developed via a modified Delphi technique and implemented at primary care clinics in Kweneng, Botswana. Sessions were video captured and independently reviewed. Response process and internal structure analysis utilized intra-class correlation (ICC) and Fleiss' Kappa. A structured log was utilized for feasibility of remote video capture. RESULTS: Eleven subjects participated. Scenarios of Lower Airway Obstruction (ICC = 0.925, 95%CI 0.695-0.998) and Hypovolemic Shock from Severe Dehydration (ICC = 0.892, 95%CI 0.596-0.997) produced excellent ICC among raters while Lower Respiratory Tract Infection (LRTI, ICC = 0, 95%CI -0.034-0.97) and LRTI + Distributive Shock from Sepsis (0.365, 95%CI -0.025-0.967) were poor. Oxygen therapy (0.707), arranging transport (0.706), and fluid administration (0.701) demonstrated substantial task reliability. CONCLUSIONS: Initial development of an assessment tool demonstrates many, but not all, criteria for validity evidence. Some scenarios and tasks demonstrate excellent reliability among raters, but others may be limited by manikin design and study implementation. Remote simulation assessment of some skills by clinic-based providers in global health settings is reliable and feasible.
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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 | 0.111 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".