A13 Skill acquisition and skill decay in medical first response skills: when more is more
Bibliographic record
Abstract
Background Tactical combat casualty care (TCCC) requires a massive effort in initial training, keeping personnel current, and ensuring those skills can be applied correctly when necessary. Unlike medical personnel, tactical personnel do not have access to civilian patient populations to ensure clinical skills remain current. There are still no evidence-based models of skill acquisition and skill decay, no understanding of mediating or mitigating factors, and more importantly no mitigation strategies in (military) medical tasks (e.g. 1 2 ). This presentation describes a methodology to quantify performance in TCCC, specifically understanding and quantifying skill acquisition and skill decay. We also present initial results showing that the currently applied retention intervals (i.e. frequency of refresher training) severely overestimate skill retention. Method The methodology we developed to quantitatively assess skill acquisition and retention comprises a skill-lab training with two objectives (figure 1). First, quantifying the technical performance by analysing the outcome and the details of the performance of the medical skill, respectively through live observation by technical observers (macro analysis) and through retrospective video analysis in ‘The Observer XT’ (Version 16, Noldus Information Technology BV, NL) by a medical expert (micro analysis). Second, quantifying the allostatic load by analysing facial expressions as well as performing a voice stress analysis. Abstract A13 Figure 1 Schematic overview of the methodology and its constituent components Recordings, both audio and video, are made using eight high-quality synchronized cameras and two microphones, integrated through the Viso software (Noldus Information Technology BV, NL). The automated emotion and action unit coding software, FaceReader (Version 10, Noldus Information Technology BV, NL) is used to analyse facial expressions. In addition, our model of Voice Stress Analysis 3 is applied to analyse the voice recordings. This combination of assessments is applied as from initial training, and subsequently in repeated measures design with intervals ranging from one month to one year. Results Initial results show that, even right after training, performance is far from consolidated. We measured the outcome lower than 80% success on certain skills, even for basic ones like tourniquet application (56% execution without critical mistakes). Furthermore, the allostatic load analysis shows we are still in the ‘teaching’ phase and not in the ‘training’ phase, that skills are not automated and still require a high amount of attentional engagement. As a side result, we also showed that the usual quantification of performance through observation by instructors overestimates performance, through conscious and unconscious biases. We show that the first three months after initial training are crucial for consolidation, and that the usual approach of yearly refreshers is not adequate. Conclusion The novel content of this project is to integrate what are usually termed ‘hard’ and ‘soft’ skills. The evaluation methodology allows for a detailed skill acquisition and retention analysis, by coupling the macro-outcome to micro-recordings of performance, coupled to facial expression and voice recordings that offer a unique insight into providers’ performance. Even preliminary results show that our current approach to both initial training and refreshers needs updating. Acknowledgment Funding from the Royal Higher Institute for Defence of the Belgian Defence under grant HFM/19–08 is acknowledged. The authors declare that there are no conflicts of interest related to this study. References Perez RS, Skinner A, Weyhrauch P, et al . Prevention of surgical skill decay. Mil Med . 2013; 178 (10 Suppl):76–86. doi: 10.7205/MILMED-D-13–00216. Branch R, Cole K. Advanced airway management skill decay: a review of the literature. AANA J . 2024; 92 (3):167–172. PMID: 38758710. Van Puyvelde M, Neyt X, McGlone F, et al . Voice stress analysis: a new framework for voice and effort in human performance. Front Psychol . 2018; 9 :1994. doi: 10.3389/fpsyg.2018.01994.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".