Multitasking in the military: Cognitive consequences and potential solutions
Bibliographic record
Abstract
Summary Multitasking—the performance of several tasks at the same time—is becoming increasingly prevalent in workplaces. Multitasking is known to disrupt performance, particularly in complex and dynamic situations, which is exactly what most military occupations entail. Because military errors can be consequential, the detrimental impact of multitasking on cognitive functioning in such contexts should be taken seriously. This review pertains to high‐consequence military occupations that require strong multitasking skills. More specifically, it highlights cognitive challenges arising from different forms of multitasking and discusses their underlying cognitive processes. Because such challenges are not expected to diminish, this review proposes context‐relevant solutions to decrease occupational workload, either by reducing the cognitive load ensuing from the to‐be‐performed tasks or by improving soldiers' multitasking abilities. To ensure effective implementation of these solutions, we stress the need to design context‐adapted tools and procedures, and to guide human resource managers in developing particular strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".