Nursing Faculty Shortage in Canada: A Review of Contributing Factors
Bibliographic record
Abstract
Background: Strong nursing faculty is paramount to promote disciplinary leadership and to prepare future nurses for practice. Our understanding of the factors associated with or predictive of nurse faculty retention and/or turnover is lacking. Purpose: The aim of this review is to identify and synthesize the existing literature on factors contributing to nurse faculty shortage in Canada and implications on nursing practice. Methods: A scoping review based on the Arskey and O’Malley’s five stage framework for scoping reviews was undertaken. Utilising the PRISMA protocol, a comprehensive and structured literature search was conducted in five databases of studies published in English.Findings: Limited through search inclusion and relevance of research, nine studies out of 220 papers met the criteria for this review and were thematically analyzed. Identified themes were: supply versus demand; employment conditions; organizational support; and personal factors.Discussion: Impending retirement of faculty, unsupportive leadership, and stressful work environments were frequently reported as significant contributing factors to the faculty shortage.Conclusions: This scoping review provide insights into how Canada’s schools of nursing could engage in grounded efforts to lessen nursing faculty shortage, both nationally and globally. We identified a gap in the literature that indicates that foundational work is needed to create context-specific solutions. The limited studies published in Canada suggests that this is a critical area for future research and funding.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.033 | 0.064 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".