Critical analysis of nurses' labour market effectiveness in Canada: The hidden aspects of the shortage
Bibliographic record
Abstract
This article proposes a critical analysis of the effectiveness of the nurses' labour market by addressing the classic dimensions of a labour market: supply, demand, and the impact of wages. Specifically, this work aims to (1) clarify the various concepts of labour shortage and present the evidence and (2) provide a critical analysis of the literature in terms of the efficiency of the nurses' labour market, while presenting descriptive statistics relevant on the supply and demand of nurses' labour. Such work elucidating the concepts and bringing a critical retrospective and prospective analysis on the subject at the pan-Canadian level constitutes an important contribution to the literature on the trends in the nursing labour market. The results suggest that this shortage in Canada was around 2.6% in 2012; it would continue until 2022 but would be reduced to 1.3% on average (corresponding to more than 46 000 nurses). Quebec would be the province with the highest vacancy rate. Besides, the analysis suggests that the postrecession period of 2008 was managed more effectively than that in the early 1990s. Measures particularly related to the provision of health services and adequate management of the workload by the institutions are to be prioritized in order to solve the shortage problem.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".