Investigating Ethical Climate and Workaholism in Healthcare
Bibliographic record
Abstract
<h3>Context:</h3> During the Covid-19 pandemic, healthcare workers (HCW) had to invest significant time and effort into their work, probably excessively so. Such a situation can be conducive of workaholic tendencies. Healthcare organizations need to be able to protect HCW from such tendencies. <h3>Objective:</h3> Using the conservation of resources theory and social learning theory, to better understand how ethical climate and organizational tenure interact to influence workaholism, and in turn, nurture higher perceived quality of care and lower intention to leave the profession. <h3>Study Design:</h3> An online Qualtrics survey was sent distributed in healthcare organizations and among HCW associations across Canada during the Omicron wave. Using AMOS 28, Structural Equation Modeling (SEM) was used to test the hypotheses of direct effect, mediation, moderation, and moderated mediation. <h3>Setting or Dataset:</h3> 887 HCW from all provinces and territories fully responded to the survey. Data is cross-sectional. <h3>Population Studied:</h3> Anyone working in healthcare from all lines of duties and speciality in Canada. <h3>Instrument:</h3> Only valid psychometric scales were used (Olson, 1998; Andreassen et al., 2012; Devoe et al., 2002; Kelloway et al., 1999). Control variables are age and gender. <h3>Outcome Measures:</h3> Workaholism, Quality of Care, Intention to Leave the Profession <h3>Results:</h3> Ethical climate had a negative relationship with workaholism, meaning it helps decrease workaholic tendencies. Ethical climate has beneficial indirect effects on both outcomes. Furthermore, organizational tenure moderated both the direct and indirect effects of ethical climate, where individuals with low tenure beneficiated significantly more from working in an organization with a strong ethical climate. <h3>Conclusion:</h3> Ethical climate is a key variable in understanding how an organizations’ climate can impact workaholic tendencies in healthcare. This in turn affects how HCWs perceive the quality of their care and their intention to remain in healthcare. Tenure, even when controlling for age, plays a major role in the relationship between ethical climate and the outcomes. As such, healthcare organizations should aim to nurture an ethical climate and make it as concrete as possible. Furthermore, new employees, even with extensive prior experience, must be made aware of the climate so they can benefit from it.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".