Are happy employees healthy employees? Researching the effects of employee engagement on absenteeism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it