Causes and effects of hospital nursing shortages to consider potential feedback effects: an umbrella review
Bibliographic record
Abstract
BACKGROUND: In Canada and internationally, health systems have experienced rising healthcare staffing shortages in recent years. Specifically, this study seeks to analyse evidence on the causes and effects of hospital nursing shortages, to consider whether shortages may be self-reinforcing. It complements an existing linear healthcare workforce logic model (Sonderegger et al., 2021) by considering whether there may be evidence that implies the existence of feedback loops (a form of system dynamics). METHODS: An umbrella review was undertaken to identify both causes and effects of hospital nursing shortages. A two-phase approach was undertaken: first, a review of all articles to identify a common list of factors, and second, a subsequent line-by-line review to ensure comprehensive coding. RESULTS: The umbrella review identified several specific issues which were both causes and effects of nursing shortages, across a number of articles. This suggests that shortages could be self-reinforcing. For policymakers, the implication is that early intervention is likely to support the resilience and retention of hospital nurses. For researchers, this study highlights the risk of biased coefficients within econometric analysis and provides a testable cross-country hypothesis for the impacts of early intervention. CONCLUSIONS: Overall, this study contributes to existing academic literature and practical policymaking by identifying evidence that nursing shortages may be self-reinforcing. Through proactive intervention to restrain the growth of workforce shortages, policymakers can support the welfare of healthcare service users and nurses themselves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".