Double-Duty Carers’ Health and Wellbeing during COVID-19: Exploring the Role of Mobility of the Care Economy in Southern Ontario, Canada
Bibliographic record
Abstract
Double-Duty Carers (DDCs) refer to people who work in the healthcare industry while also providing unpaid care to relatives, friends, or neighbours. The expectations placed on DDCs is expected to grow, and these employees already experience a high caring burden. As such it is important to understand how best to support their health and wellbeing. This paper explores DDCs' wellbeing during the COVID-19 pandemic, focusing an understudied factor: their mobility constraints. Following the Mobility of the Care Economy framework and a qualitative research design, it does so through a thematic analysis of 16 semi-structured interviews with female DDCs in Southern Ontario, Canada. Once data saturation was reached, three mobility pathways during the pandemic were identified, all of which negatively affected DDCs wellbeing. First, some COVID-19 policies (e.g., testing requirements) resulted in increased mobility demands and increased spatiotemporal constraints. Second, the closure of institutions that care for dependents (schools, daycares, day centres) resulted in forced reduced mobility, which increased financial stress. Finally, indirect mobility effects were identified: the reduced mobility of other informal carers increased the workload and emotional strain on DDCs. The paper concludes with a discussion of mobility-related policies that could improve DDC wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".