A better way: training for direct observations in healthcare
Bibliographic record
Abstract
Direct observation is valuable for identifying latent threats and elucidating system complexity in clinical environments. This approach facilitates prospective risk assessment and reveals workarounds, near-misses and recurrent safety problems difficult to diagnose retrospectively or via outcome data alone. As observers are an instrument of data collection, developing effective and comprehensive observer training is critical to ensuring the reliability of the data collection and reproducibility of the research. However, methodological rigour for ensuring these data collection properties remains a key challenge in direct observation research in healthcare. Although prior literature has offered key considerations for observational research in healthcare, operationalising these recommendations may pose a challenge and unless guidance is also provided on observer training. In this article, we offer guidelines for training non-clinical observers to conduct direct observations including conducting a training needs analysis, incorporating practice observations and evaluating observers and inter-rater reliability. The operationalisation of these guidelines is described in the context of a 5-year multisite observational study investigating technology integration in the operating room. We also discuss novel tools developed during the course our project to support data collection and examine inter-rater reliability among observers in direct observation studies.
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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 | 0.178 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".