A Workplace Environmental Scan of Employed Carers During COVID-19
Bibliographic record
Abstract
The carer-employee experience has undergone multiple shifts during the COVID-19 pandemic. This study seeks to understand how changes in the workplace as a result of the pandemic have impacted employed carers with their ability to perform both care obligations and paid work responsibilities. Using an online workplace-wide survey at a large Canadian firm, we conducted an environmental scan of: the current state of workplace supports and accommodations, supervisor attitudes, and carer-employee burden and health. Our findings demonstrate that while employees are generally in good health, care burden and time spent caregiving has been higher during COVID-19. Notably, employee presenteeism is higher during the pandemic than it was previously, with carer-employees experiencing significantly reduced levels of co-worker support. The most common workplace adaptation to COVID-19, work-from-home, was preferred by all employees as it allowed greater schedule control. However, this comes at the cost of reduced communications and sense of workplace culture, especially for carer-employees. We identified several actionable changes within the workplace, including: greater visibility of existing carer resources, and standardized training of managers on carer issues.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".