Assisted-Transport Caregiving and Its Impact Towards Carer-Employees
Bibliographic record
Abstract
Assisted-transport is the most common informal caregiving task and will be in greater demand due to an aging society. One population group that predominantly covers the demands of informal eldercare while working full time in the paid labor force are carer-employees. The developing carer-employee literature addresses: the health risks for carer-employees; employers of carer-employees, and policy/program interventions. Little research focuses on assisted-transport, which impacts health. This study begins to fill the gap by addressing the following objectives: (1) develop a socioeconomic profile of carer-employees performing assisted-transport tasks; (2) identify any gender differences based on the profile, particularly employment and caregiving traits; (3) examine behavioral factors that increase the likelihood of conducting assisted-transport caregiving, and; (4) determine whether carer-employees are more likely to be overwhelmed from assisted-transport caregiving. Descriptive statistics and logistic regression were used to analyze Statistics Canada's General Social Survey Cycle 26: Caregiving dataset (2012). Compared to general carer-employees, assisted-transport carer-employees have higher education, household income, and caregiving hours per week and feel more tired and overwhelmed from caregiving. Gender gaps exist based on socioeconomic and caregiving characteristics. Logit results show that female carer-employees are more likely to perform assisted-transport caregiving and feel overwhelmed. Carer-employees conducting assisted-transport caregiving are more likely to be overwhelmed than those who do not.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".