A13 Skill acquisition and skill decay in medical first response skills: when more is more
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».