Characteristics of early career health researchers and experiences of burnout during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic disrupted research globally. How it impacted Canadian early-career health researchers (ECHRs) remains unclear. We administered a survey to understand the composition of ECHRs in Canada, their job experiences, and experiences of burnout during the COVID-19 pandemic. Methods A cross-sectional survey was conducted in May 2023 of Canadian ECHRs defined as within 7 years of their first independent research position. Quantitative analyses included a description of respondents by research pillar, socio-demographic and workplace characteristics, and the prevalence of burnout, disengagement or exhaustion. Sample characteristics were compared to national data on ECHRs from a Canadian funding agency. Thematic analysis of free-text responses was also conducted. Results A total of 225 respondents met the eligibility criteria. Most respondents were assistant professors and characteristics of our sample were like the national data. The COVID-19 pandemic posed many challenges to student recruitment, and emotional support of students, with over half of the respondents reporting a moderate to significant decline in mental health compared to pre-pandemic. A significant proportion of respondents were experiencing high burnout (62%, 95%CI:56-67%), exhaustion (64%, 95%CI: 57-70%) or disengagement (91%, 95%CI: 87-95%). Thematic analysis identified three themes: ongoing benefits/problems preceding the pandemic, unintended outcomes of strategies to manage/prevent/contain COVID-19, and reasons to stay in their current position. Conclusions Our survey revealed that Canadian ECHRs reported many diverse challenges during the COVID-19 pandemic and high burnout, putting the sustainability of this workforce at risk. Improved systems are needed to understand the long-term impacts and support the future of the Canadian health research ecosystem.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".