Bibliographic record
Abstract
The Canadian Institute for Health Information says the number of registered nurses working in Canada declined slightly between 2000 and 2001, from 232 566 to 231 512. Five provinces experienced gains, with increases ranging from 15 nurses (1.2%) in Prince Edward Island to 753 (3.4%) in Alberta. Decreases in the remaining provinces and territories ranged from 145 nurses (1.7%) in Nova Scotia to 345 (4%) in Saskatchewan. The proportion of nurses working in casual positions has decreased since 1997, from 18.3% to 12.8%, while the proportion working in full-time positions has increased from 49.8% to 53.2%. In both 1997 and 2001, about one-third of RNs worked in part-time positions. Newfoundland RNs are most likely to be employed full time (74.3%), followed by those in New Brunswick (58%) and Nova Scotia (57.1%). Alberta and Manitoba count the lowest proportion of full-time RNs (46%). Fifteen percent of nurses reported having 2 or more employers in 2001. In 2001, 63.4% of employed RNs worked in hospital settings, a proportion that has remained relatively stable since 1997. The majority (84.1%) work in direct patient care, with 7% in administration and 3.8% in education. Since 1997, the proportion of employed RNs with a diploma as their highest level of education has decreased from 79% to 74%, while the proportion with undergraduate degrees has increased from 20% to 24%. In 2001, 2% of RNs held master's or doctoral degrees. Fifty-seven percent of management positions were filled by RNs with diplomas in 2001, while 38% and 6%, respectively, were filled by those with undergraduate and graduate degrees. — Shelley Martin, Senior Analyst, CMA Research, Policy and Planning Directorate
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.129 | 0.052 |
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".