Investing in Canada’s nursing workforce post-pandemic: A call to action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nurses represent the highest proportion of healthcare workers globally and have played a vital role during the COVID-19 pandemic. The pandemic has shed light on multiple vulnerabilities that have impacted the nursing workforce including critical levels of staffing shortages in Canada. A review sponsored by the Royal Society of Canada investigated the impact of the pandemic on the nursing workforce in Canada to inform planning and implementation of sustainable nursing workforce strategies. The review methods included a trend analysis of peer-reviewed articles, a jurisdictional scan of policies and strategies, analyses of published surveys and interviews of nurses in Canada, and a targeted case study from Nova Scotia and Saskatchewan. Findings from the review have identified longstanding and COVID-specific impacts, gaps, and opportunities to strengthen the nursing workforce. These findings were integrated with expert perspectives from national nursing leaders involved in guiding the review to arrive at recommendations and actions that are presented in this policy brief. The findings and recommendations from this policy brief are meant to inform a national and sustained focus on retention and recruitment efforts in Canada.
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.
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 it