Work–family conflict/family–work conflict, job stress, burnout and intention to leave in the hotel industry in Quebec (Canada): moderating role of need for family friendly practices as “resource passageways”
Bibliographic record
Abstract
This study examines how the need for family friendly practices contribute in increasing the effects of work–family conflict (WFC) and family–work conflict (FWC) on job stress, burnout, and intention to leave in the hotel industry in Quebce (258 staff). The essential results indicate that the perception of a need for childcare moderates the relationship between FWC, job stress, and burnout. Also, employees wanting to have a compressed workweek and part time measures are exposed to more stress related to WFC/FWC. Finally, the four measures can constitute resources passageways in order to reduce the work-family interference, job stress, burnout and therefore the intention to leave. Theoretically, he results extend this line of theorizing by highlighting the importance of subjective needs for family friendly policies, as ‘resource caravan passageways’, in the work–family interface and job outcome processes. The perception of a desire or need for these measures offers a new understanding of these practices. Practically, identifying who is more sensitive to family friendly measures would enable organizations or employers to allocate supportive resources more adequately by targeting those employees who are most in need of such practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".