Does Simulator Sickness Impair Learning Decision Making While Driving a Police Vehicle? | Le mal du simulateur: un frein à l’apprentissage de la prise de décision en conduite d’un véhicule de police?
Bibliographic record
Abstract
The use of driving simulators is an innovation for police training in Quebec. There are some issues related to their impact on training objectives. This article presents the results of a study involving 71 police cadets who participated in six training sessions with a driving simulator. The training sessions were designed to competencies development to make decisions during emergency driving and pursuit. The nature and consequences of the discomfort experienced by the participants is described. The results highlight the importance of the initial training session. Issues related to providing trainees with adequate support are discussed. Le simulateur de conduite est un outil pédagogique novateur pour la formation policière au Québec. Il comporte des enjeux à l’égard de l’atteinte des objectifs pédagogiques visés. L’article présente les résultats d’une recherche menée auprès de 71 aspirants policiers ayant participé à une formation de six séances au simulateur de conduite visant le développement des compétences en matière de prise de décision en conduite d’urgence et en poursuite. Les résultats dressent un portrait des malaises ressentis par les participants et de leurs impacts. Les résultats montrent également l’importance de la première séance. La discussion s’attarde aux enjeux liés à l’accompagnement des apprenants.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".