Effective Strategies to Reduce Employee Absenteeism Amongst Canadian Female Employees
Bibliographic record
Abstract
High absenteeism in female employees costs Canadian hospitals millions of dollars annually. Leaders of Canadian hospitals who lack strategies to reduce absenteeism in female employees witness significant financial losses in their organizations. Grounded in Herzberg's two-factor theory, the purpose of this multiple case study was to explore strategies Canadian hospital leaders used to reduce absenteeism in female employees. Data were collected from semistructured interviews, annual reports, and publicly available datasets relating to hospital retention strategies and were analyzed using a thematic analysis. Four themes on strategies to reduce absenteeism emerged: creating a supportive stance towards absenteeism, investing in mental health and wellness resources, adopting a whole-person approach, and providing aid for childcare. A key recommendation is for leaders to adopt a supportive stance toward absenteeism, focusing on well-being over absence. The implication for positive social change from decreased costs relating to high female employee absenteeism could result in Canadian hospitals having increased resources to improve their services to local communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".