Bibliographic record
Abstract
Happier Nurses Means Better CareCanadians love nurses.Time and time again, pollsters have found that nursing ranks among the most trusted professions.In fact, for many Canadians, nurses act as the barometer of how well the healthcare system is working.I remember conducting a focus group among members of the public on their attitudes toward the healthcare system.They were talking about how they got information about issues in the system, when one young mother of two summed it up perfectly, "Forget about what politicians or the newspapers say.If I walk into an emergency room and see that the nurses are running off their feet … they're too stressed out for a smile, I know there's a problem."So what's our barometer forecasting?The results of a national survey of 200 nurses conducted last fall* paint a telling picture.When asked to suggest the most important healthcare issues facing Canada, nurses overwhelmingly point to a lack of healthcare staff and work overload (30%), while another two-inten (20%) point to the underlying issue of government cutbacks and funding shortages.No other healthcare issue was mentioned by more than 5% of nurses.In fact, when asked directly, three-quarters (75%) of nurses polled believe there are human resource shortages in nursing.The impact of the shortages has a real effect on the quality of life for nurses, and subsequently the quality of care they are able to provide.Nurses themselves explain that shortages in their profession mean an increased workload (mentioned by 40% of nurses), lower quality care for Canadians (30%) and high stress for nurses (10%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".