Emergency responses for a health workforce under pressure: Lessons learned from system responses to the first wave of the pandemic in Canada
Bibliographic record
Abstract
The global health workforce crisis, simmering for decades, was brought to a rolling boil by the COVID-19 pandemic in 2020. With scarce literature, evidence, or best practices to draw from, countries around the world moved to flex their workforces to meet acute challenges of the pandemic, facing demands related to patient volume, patient acuity, and worker vulnerability and absenteeism. One early hypothesis suggested that the acute, short-term pandemic phase would be followed by several waves of resource demands extending over the longer term. However, as the acute phase of the pandemic abated, temporary workforce policies expired and others were repealed with a view of returning to 'normal'. The workforce needs of subsequent phases of pandemic effects were largely ignored despite our new equilibrium resting nowhere near our pre-COVID baseline. In this paper, we describe Canada's early pandemic workforce response. We report the results of an environmental scan of the early workforce strategies adopted in Canada during the first wave of the COVID-19 pandemic. Within an expanded three-part conceptual framework for supporting a sustainable health workforce, we describe 470 strategies and policies that aimed to increase the numbers and flexibility of health workers in Canada, and to maximise their continued availability to work. These strategies targeted all types of health workers and roles, enabling changes to the places health work is done, the way in which care is delivered, and the mechanisms by which it is regulated. Telehealth strategies and virtual care were the most prevalent, followed by role expansion, licensure flexibility, mental health supports for workers, and return to practice of retirees. We explore the degree to which these short-term, acute response strategies might be adapted or extended to support the evolving workforce's long-term needs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".