Wages and Educational Credentials: The Case of Registered Nurses in Canada
Bibliographic record
Abstract
(Preliminary draft) Abstract: The labour market for Registered Nurses represents an important area for research. Recent media reports discuss shortages in the supply of registered nurses in Canada and the United States and the aging population will only increase the demand for nursing services in the future. The purpose of this paper is to examine the determinants of earnings of baccalaureate nurses (BN) and hospital-based diploma nurses (RN) to see if there is a higher return to education in the BN program among Canadian nurses. This study builds on an existing literature which is mostly related to nurses in the United States. Significantly fewer studies have examined nursing in Canada and this research extends that. Using the 1996 Canadian Census Public Use Microdata File (PUMF) the determinants of nursing wages are estimated for men and women separately using a basic human capital model. This paper finds that there are systematic differences between immigrants, visible minorities and province of employment for both male and female nurses and this supports the existing literature. Wages and Educational Credentials: The Case of Registered Nurses in Canada 1.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".