Impact of the COVID-19 pandemic on the Canadian healthcare workforce: a rapid evidence synthesis of key considerations, lessons learned, and promising practices to address the healthcare workforce crisis
Bibliographic record
Abstract
BACKGROUND: The ongoing impacts of the COVID-19 pandemic on Canada's healthcare workforce and service delivery necessitate focused health system planning and delivery that prioritizes coordination, collaboration, and evidence-based strategies. A rapid evidence synthesis was commissioned by Health Canada to determine the impacts of the pandemic on the healthcare workforce and to identify promising strategies and innovations that mitigate these challenges. METHODS: Two, sequential rapid evidence syntheses were conducted between October 2022 and March 2023 using methodologies aligned with Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) guidelines. The first review (October-November 2022) focused on the impacts of COVID-19 on Canadian healthcare workers and mitigation strategies, while the second (November 2022-March 2023) broadened the scope to international interventions. Findings were organized by impact level (individual, organizational, system). Quality assessment of sources was not performed. RESULTS: We included 176 and 31 sources, respectively in the analysis. Sources identifying impacts of the COVID-19 pandemic described significant mental health impacts on healthcare workers, alongside changes in demand and supply of services, physical health challenges, and shifts in scopes of practice or care models. Interventions were primarily targeted at the individual or organizational level and included mental health support, training and upskilling, enhanced organizational communication and workforce planning initiatives. System-level interventions were less common, and most interventions lacked robust evaluation or evidence-informed design. CONCLUSIONS: This review highlights a significant gap in literature regarding evaluated interventions to address healthcare workforce challenges during the pandemic. While numerous sources document the adverse impacts on healthcare workers, detailed reports on specific interventions are scarce. Most interventions focus on workforce planning, education, practice scopes, recruitment and technology integration. The research underscores the need for comprehensive recommendations addressing social and mental health support, workplace safety, organizational communication and pandemic preparedness. These recommendations are vital for developing future workforce strategies, thus enabling policymakers and healthcare leaders to effectively respond to current and future healthcare challenges. This strategic approach will enhance system resilience and improve healthcare delivery across Canada.
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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 | 0.170 | 0.324 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".