Understanding sickness absence in nurses and personal support workers: Insights from frontline staff and key informants in Northeastern Ontario
Bibliographic record
Abstract
BACKGROUND: Nurses and personal support workers (PSWs) have high sickness absence rates in Canada. Whilst the evidence-based literature helped to identify the variables related to sickness absenteeism, understanding "why" remains unknown. This information could benefit the healthcare sector in northeastern Ontario and in locations where healthcare is one of the largest employment sectors and where nursing staff have high absence and turnover rates. OBJECTIVE: To identify and understand the factors associated with sickness absence among nurses and PSWs through several experiences while investigating if there are northern-related reasons to explain the high rates of sickness absence. METHODS: In this descriptive qualitative study, focus group sessions took place with registered nurses (n = 6), registered practical nurses (n = 4), PSWs (n = 8), and key informants who specialize in occupational health and nursing unions (n = 5). Focus group sessions were transcribed verbatim followed by inductive thematic analysis. RESULTS: Four main themes emerged, which were occupational/organizational challenges, physical health, emotional toll on mental well-being, and northern-related challenges. Descriptions of why such factors lead to sickness absence were addressed with staff shortage serving as an underlying factor. CONCLUSION: Despite the complexity of the manifestations of sickness absence, work support and timely debriefing could reduce sickness absence and by extension, staff shortage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".