The effect of work-family conflicts on withdrawal behaviours in the healthcare sector
Bibliographic record
Abstract
Building on two competing theoretical frameworks (the Stress Management Model and the Domain-Specific Predictor-to-Outcomes Model) we developed a model to test the incidence of six forms of work-family interferences (time-, strain- and behavioral-based family interferences with work – FIW – and work interferences with family – WIF) on four withdrawal behaviours (absenteeism, late arrival, early departure, and work interruptions) using cross-sectional data from a sample of nurses working for a regional hospital in Canada.Data was collected through a paper-and-pen questionnaire and 402 complete questionnaires were analyzed using multivariate regression. Results suggest that the domain-specific predictor-to-outcomes model produces a better explanation of the dependent variables, as the strain-based FIW explain respondents’ absenteism and work interruptions. We didn’t find support for the other hypotheses deduced from this model (influence of time- and behavior-based FIW on withdrawal behaviors) as well as those steming from the stress management model (time-, strain- and behavior-based WIF influence on withdrawal behaviors).
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".