Workplace Wellbeing and Sense of Mattering Among Small and Medium Enterprise Workers in Canada
Bibliographic record
Abstract
Small and Medium Enterprises (SME) are an under researched area when it comes to workplace wellbeing in Canada. The current research used the Job-Demands Resource Framework (JD-R) to study how a Community Psychology concept, sense of mattering, relates to workload, and indicators of wellbeing (i.e., burnout and flourishing). Specifically, this study tested whether sense of mattering moderates the association between workload and both burnout and flourishing. Moderated mediation models were also tested to see if workload is indirectly associated with burnout and flourishing through distress, while being moderated by sense of mattering. Questionnaires were administered to 2,500 Canadian SME workers at a single time point. No interaction effects were found for mattering as a moderator. Greater workload was directly associated with higher burnout and weaker sense of flourishing. Workload was indirectly associated with burnout and flourishing via distress. Post-hoc analyses revealed that workload had an indirect effect on burnout via mattering and distress. The findings of this study expand on prior research by testing specific interacting components of the JD-R theory and incorporating relevant community psychology principles. These findings may also have practical applications for understanding and improving individual wellbeing and organizational outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".