Pressed for Time: Physiological Indicators of Care Aides’ Mental Workload in Response to Simulated Pressures in Long-Term Care Homes
Bibliographic record
Abstract
BackgroundMental workload is an important indicator of an individual's interaction with task demands. Care aides in long-term care (LTC) settings frequently report excessive demands imposed on their daily work due to challenging resident behaviours and organizational expectations. Understanding mental workload in these contexts is key to predicting staff strain and guiding support strategies.MethodsTwenty-eight care aides from six LTC homes in New Brunswick, Canada, participated in a simulated care scenario involving common challenges encountered when completing resident care. Two physiological markers of mental workload, namely heart rate variability (HRV) and pupil dilation, were continuously measured across five experimental stages, each designed to elicit different cognitive and emotional demands. Hierarchical mixed-effects models assessed the impact of demographic variables and experimental stages on mental workload.ResultsMental workload increased significantly, as indicated by decreased HRV and increased pupil diameter, when the care aide was required navigate impossible requests made by the resident. Contrary to expectations, resistance to care, verbal aggression, direct time pressure, and intervention by a supervisor did not significantly influence physiological correlates of mental workload.ConclusionThese findings suggest that creative problem-solving, such as responding to impossible resident demands, may be more mentally taxing than expected stressors like aggression or time pressure. To manage mental workload, staff should be prepared and supported to adapt creatively under pressure. Further efforts should be made to understand the relationship between increased mental workload and cumulative stress in care aides.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".