Carers in the hybrid workplace: examining perceived inclusion and inclusion climate
Bibliographic record
Abstract
Purpose This article aims to examine differences between the perceived inclusion of carers and non-carers and their perception of the inclusion climate in a hybrid organization. The influence of different inclusion climate dimensions on the perceived inclusion of carers and non-carers is also investigated. Design/methodology/approach A quantitative survey was conducted among 5,311 Government of Quebec employees, including 957 informal carers with responsibility for caring for a member of their family or community. The sense of organizational inclusion was measured using Chung et al.’s scale (2020). Nishii’s scale (2013) was used to measure the climate of inclusion. T-tests, ANOVAs and hierarchical regressions were also performed. Findings The results show that carers have significantly lower perceived inclusion than non-carers. Carers who telework three or more days a week report higher perceived inclusion than those who telework two or fewer days. The results suggest that the integration of differences in the workplace has the strongest influence on perceived inclusion for both carers and non-carers, followed by inclusion in decision-making. Originality/value This is the first study to specifically address workplace inclusion of carers, a group rarely addressed in equity, diversity and inclusion studies to date. By considering new identity factors in the context of the hybrid work practices, this research makes an important contribution to broadening the traditional conception of diversity and in the new workplace.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".