Determinants of healthcare employees’ preference to continue teleworking after the COVID-19 pandemic: a cross-sectional study using hierarchical regression
Bibliographic record
Abstract
Abstract Employees’ post-pandemic telework preference is an important consideration for navigating post-pandemic work arrangements and can inform organizational planning and workforce management. A cross-sectional survey of employees (n=400, participation rate =36.4%) of a regional health authority who teleworked during the COVID-19 pandemic was conducted. The most common post-pandemic telework preference was all the time (52%) followed by over half but not all the time (32%) and less than half the time or not at all (16%). Using hierarchical multinomial logistic regression models and less than half the time or not at all as the reference outcome, being a provider of direct patient care and productivity while teleworking were strong determinants of post-pandemic telework preference while two or more weekly teleconference hours, work-life balance and having one or more people over five years of age in the home while teleworking were moderate determinants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".