Synergy of wearable technologies and proficiency-based progression for effecting improvement in procedural skill training
Bibliographic record
Abstract
The move from time-based to competence-based training has been limited by practical (often resource) issues and by the variability of effect offered by different training methodologies. Two independent advances, one technical (wearable recording devices (WRDs)) and the other methodological (proficiency-based progression—PBP),1 may act synergistically to enable consistently effective training in procedural skills. In this article, we describe our ongoing work in which both are integrated in ‘real-world’ training and the potential for these together to transform training in procedural skills. Although the proficiency of physicians undertaking procedural skills directly influences patient outcome,2 valid assessment of doctors’ procedural skills is yet a reality. The WRD alone will not be sufficient (as it simply enables acquisition of more data) but these devices can be central to acquiring digital recordings without consuming the learner's attention. Gallagher and colleagues have described PBP for training in procedural skills. This approach consistently achieves greatly superior training effect—including clinical performance—compared with other methods of competency assessment approaches3 but requires the development of unambiguously defined and detailed procedure-specific metrics and errors, so-called ‘procedure characterisation’.1 The success of PBP is dependent on the definition and recognition of specific observable behaviours. In practice, this requires direct (and resource-consuming) expert observation or video acquisition and analysis. The emergence low cost, high-quality WRDs may address this impediment to widespread introduction of PBP. This synergy may enable doctors to acquire a cumulative personal ‘visual data set’ suitable for …
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".