Evidence-based strategies in occupational health: applying meta-analytic and qualitative methods to identify and understand sickness absence among nurses and health care aides with considerations for Northeastern Ontario
Bibliographic record
Abstract
Purpose: Compared to other employees, nurses and health care aides (HCAs) have the highest sickness absence rates in Canada yet the phenomenon remains insufficiently studied. Furthermore, the potential influence of geography on sickness absence has received scant attention. Guided by the Evidence-Based Practice in Occupational Health Psychology framework, this investigation aimed to identify factors associated with sickness absence, understand how they occur, and determine factors that may be specific to communities in northeastern Ontario. Methods: A systematic review identified relevant studies through structured search strategies, article screening, and quality testing. Pooled statistics in the form of odds ratios and confidence intervals were computed. Follow-up analyses examined heterogeneity (Q& I2). Qualitatively, focus group sessions were held with registered nurses (n= 6), registered practical nurses (n= 4), HCAs (n= 5), and key informants specialized in nursing, occupational health, disability management, and rehabilitation (n= 5). Nursing personnel were recruited from hospitals and long-term care facilities. Narrative data were analyzed using thematic analysis. Results: Meta-analytic searches yielded 812 studies, of which 27 met eligibility, and 11 variables that influenced the odds of sickness absence in a statistically significant manner (p< .05). Variables include: sex, occupation, health rating, previous sick leave, musculoskeletal pain, poor mental health, fatigue, night shifts, pediatric and psychiatric units, increased occupational demand, and work support. Poor health rating was highly heterogeneous (p< .05; I2= 82.77%). Thematic analysis revealed four primary themes: (1) Organizational factors including exposure to infectious diseases, shift work, safety climate, and work setting; (2) the jobs’ physical impact, mainly musculoskeletal pain; (3) psychological/mental impact including guilt, anxiety, and burnout; and (4) factors unique to northeastern Ontario including poor weather and road conditions, especially for HCAs providing home care, and the limited opportunity of interconnected health care networks where employers make staff available during worker shortages. Factors leading to sickness absence were described, with staff shortage serving as an important underlying contributor. Conclusion: This investigation points to the complexity and intricacy of factors influencing sickness absences. The qualitative results helped deepen the understanding of the quantitative findings, while considering northern-specific factors. Several concerns were attributed to staff shortages.
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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 | 0.303 | 0.455 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.039 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".