Are happy employees healthy employees? Researching the effects of employee engagement on absenteeism
Bibliographic record
Abstract
In 2007, a survey was conducted to measure the levels of workplace engagement for British Columbian civil servants. Following the Heskett et al. model of the “service profit chain” (1994, 2002), the government's primary concerns were the increasing attrition rates and their effects on service delivery. Essentially, the model demonstrated that employees who were more engaged were more committed to their work and more likely to stay within the civil service and that this culminated in improved customer service. Under the joint rubrics of absenteeism and job satisfaction, this study uses a construct of engagement (i.e., job satisfaction) to test whether different levels of engagement have any effect on the amount of sick time (absenteeism) an employee incurs. Specifically, the author looks at whether there is any correlation between the amount of sick time used and an individual's level of engagement and proposes that there is an inverse negative relationship: as job engagement increases, sick time used decreases. Testing the old adage “A happy employee is a healthy employee,” this research demonstrates that, though a more engaged employee may use less sick time, the differences in use between highly engaged employees and those not engaged are fairly marginal and that correlations are further confounded by a host of other (often missing) factors.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".