Assessing Health Risk Areas and Activity-Travel Behaviour of Carer-Employees
Bibliographic record
Abstract
Carer-employees are defined as individuals who provide unpaid care to a disabled / ill dependent person(s) while working full-time in the paid labour force. In Canada, there are 6.1 million carer-employees, many of which are experiencing work-life balance struggles, which may result in ill-health. To minimize negative impacts, there is interest in developing caregiver-friendly workplace policies (CFWPs) as an intervention strategy to improve CEs’ work-life balance. However, the effectiveness of CFWPs are still in their infancy and often only focus on the work dimension. One of the most critical dimensions that have not yet been assessed is the activity-travel behaviour of carer-employees, which is largely impacted by the assisted-transport demands of their care-recipient. To contribute to filling in this gap, this dissertation addresses the following objectives: 1) develop an activity-travel behaviour profile of carer-employees using sociodemographic and caregiving characteristics; 2) identify spatial locations with potentially high assisted-transport demand while suggesting new areas to improve mobility independence of care-recipients, and; 3) create and apply a mixed-methods framework that classifies the actual activity-travel behaviour of carer-employees. The purposes of all three objectives are to: contribute to closing the literature gap; visually inform decision-makers and health planners, and; efficiently develop caregiver-friendly transport policies (CFTPs). Highlighted findings show that carer-employees conducting assisted-transport have lower income and are more likely to be tired and overwhelmed than those not performing the transport task (Objective 1). In Hamilton metropolitan area, 38% of the older adult population are not within immediate reach to a vital service, and another 15% are located in potentially high assisted-transport demand areas. Suggested areas for service implementation would improve access for older adults by 18% (Objective 2). Lastly, the framework has classified and ranked three types of activity-travel behaviours (Objective 3). All of these findings have led to the discussion of a multi-pronged implementation strategy for uptake of CFTPs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".