Feasibility of Using Existing Statistics Canada Surveys to Describe the Health and Work of Nurses
Bibliographic record
Abstract
Reorganization of nurses' work has raised questions about the effects of working conditions on their health. Nurses, for example, are more likely to miss work because of illness and disability than employees in other occupations. The overall purpose of this descriptive study was to investigate the feasibility of using existing Statistics Canada surveys regularly to describe and monitor the health and working conditions of nurses. Our findings identified significant limitations in existing Statistics Canada surveys, for the study of nurses, including nonspecific or no occupational coding, small samples and partial content related to the work environment. As a result, some estimates would need to be accompanied by statements indicating that the findings do not meet quality standards and that the conclusions would be unreliable and most likely invalid. Additional data are required for a comprehensive assessment of the health status of nurses and the work environment factors that influence their health. These data can be obtained through several vehicles, including using over-sampling strategies for extant and recurring Statistics Canada surveys, adding additional content to those surveys or implementing new surveys specific to nurses and their work. The authors describe the advantages and disadvantages of each of these approaches and conclude that monitoring the health and work environment of nurses in Canada in sufficient detail to inform policy decisions requires a dedicated national survey.
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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 | 0.050 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".