Profiles of burnout and work engagement in a public service organization: Nature, drivers, and outcomes
Bibliographic record
Abstract
Background: The Canadian Federal Public Service Workplace Mental Health Strategy (the Strategy) seeks to measure, report, and improve employee psychological health, recognizing the National Standard of Canada for Psychological Health and Safety in the Workplace (the Standard) as a starting point. The present research introduced a new survey battery for the assessment of employee psychological health as profiles of burnout and work engagement. It also considered a wide range of predictors aligned with the Standard and several outcomes in accordance with the Job Demands-Resources (JD-R) Model to support the Strategy. Data and methods: A total of 4,781 Statistics Canada employees completed an Employee Wellness Survey in late 2021, during the COVID-19 pandemic, for a response rate of 58%. Additional sociodemographic variables were linked from human resource databases. Survey weights were applied to adjust for non-response. Results: Latent profile analysis uncovered four employee psychological health profiles, ranging from employees who were thriving (15%) to those who were doing well (34%), moving along (38%), or struggling (13%). Job autonomy, role clarity, person-job fit, work-life interference, and workplace incivility -- all workplace psychosocial factors aligned with the Standard -- were consistently associated with profile membership, as expected, and outcome levels were systematically less favourable from the thriving profile to the struggling profile. Interpretation: The results support the validity of the employee psychological health profiles and predictors of profile membership, meeting expectations based on the JD-R literature. Key predictors can serve as metrics to monitor and as targets for workplace interventions designed to improve employee psychological health in support of the Strategy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".