Work-life conflict and intention to leave among teachers: The role of working time arrangements
Bibliographic record
Abstract
Attrition is a salient issue in the teaching profession, which also observes important work-life conflict. This paper assesses the influence of working time arrangements on teacher work-life conflict and intention to leave. Our descriptive results indicate that teachers experience high levels of work-family conflict and indicate moderate intention to leave. We ran ordinal logistic regressions to assess the impact of various working time arrangements onto teacher work-family conflict and intention to leave. After adjusting for sociodemographic variables, effective use of flexible hours was associated with lesser odds of experiencing conflict, while unavailability of reduced working time and study leaves was associated with greater odds of experiencing conflict. Access to, and effective use of personal/family leaves was associated with less intention to leave. However, not being able to reduce one’s work hours or to access a study leave was associated with increased intention to leave. Overall, our results point to a need for more control over the amount of hours they work, to attend to non-work needs. Our findings shed light on the reasons behind teacher attrition with regards to work-life conflict. These findings reinforce the necessity for a greater access to leaves, voluntary reduced working time and flexible hours for teachers. Investing in these key resources may reduce teacher work-life conflict and intention to leave, and thus contribute to teacher retention in the profession.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".