Impact of individual and institutional factors on wage rate for nurses in Canada: is there a monopsony market?
Bibliographic record
Abstract
Previous studies on Canadian nurse wages were limited to individual factors and did not take into account contextual factors such as hospital market share, labour market size or unionization. Based on market share, some refer to the nursing labour market as a monopsony, which depresses wages and might explain the shortage. However, this has not yet been tested empirically in the Canadian Registered Nurse (RN) labour market. This article aims to fill this gap by using the microdata files of the Labour Force Survey for the years 2010–2012 and the multilevel analysis to shed light on this issue. The contribution of this work is that it takes into account both individual and contextual variables to try to explain nurses’ hourly wage. In accordance with the monopsony model, we hypothesize a negative correlation between hourly wage and level of market share; i.e. monopsony employers would pay a lower wage rate. The results do not support the monopsony model to explain nursing labour shortage: there is no statistically significant relation between RN wages and market share; no relation was found for market size either. This suggests that an explanation for RN labour shortage must be investigated elsewhere.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".