Profiling caregivers: Caregiving workload, mobility, stress, and remote work difficulties
Bibliographic record
Abstract
The increasing focus on the urban dimensions of care has brought attention to mobility as a crucial aspect. However, traditional origin–destination and time-use surveys often overlook the nuanced and diverse aspects of care-related mobility. They fail to account for the variety of care tasks, socioeconomic conditions, spatial contexts, and relational dynamics that shape different forms of care-related movement. Our article aims to contribute to filling these gaps by analyzing caregivers’ mobility, caregiving tasks, and sociodemographic characteristics. Using a survey in Chile that compares a pre-pandemic scenario with the first reaction to the pandemic, the article uses hierarchical clustering to find caregiving-related profiles and a joint multivariate model to identify observed and unobserved effects impacting the level of stress, ease of movement, and struggle to engage in paid work from home. Our analysis identifies four distinct caregiving mobility profiles, revealing significant disparities. Caregivers with heavier workloads and limited resources experienced the greatest challenges, including restricted mobility, higher stress, and difficulty managing remote work. Our model shows that gender is a critical factor influencing stress, mobility, and work-from-home struggles, even after accounting for socioeconomic and behavioral factors. Individuals less concerned about COVID-19 mobility restrictions reported lower stress levels. Lower stress levels were reported by those less concerned about COVID-19 restrictions, while stress was notably higher among caregivers for individuals with special needs and young children (0–6 years). Connectivity issues further intensified remote work challenges. These findings underscore the need for urban mobility planning and policies that recognize caregiving as a relational activity shaped by spatial and social dynamics, emphasizing the diverse impacts on caregivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".