When the labs closed: graduate students’ and postdoctoral fellows’ experiences of disrupted research during the COVID-19 pandemic
Bibliographic record
Abstract
Government imposed lockdown measures in response to the COVID-19 pandemic resulted in widespread laboratory closures. This study aimed to examine the impact of this disruption on graduate students and postdoctoral fellows completing laboratory-based research in Canada. We used an anonymous online survey and semi-structured interviews to document the experiences of graduate students and postdoctoral fellows during laboratory closures and following the transition to working from home. We employed a mixed-method approach using survey and interview data to identify shared experiences, concerns, and supports. The emotions reported by respondents at different points during laboratory closures align with the Kübler-Ross model of grief following change. Respondents describe closure processes as chaotic and confusing, primarily resulting from inconsistent communication. Respondents reported increased indications of distress while working from home. Concerns about how COVID-19 might impact trainees were identified, including decreasing competitiveness of applicants while limiting future employment opportunities. Finally, we outline five types of supports that can be implemented by supervisors and administrators to support graduate students and postdoctoral fellows to return to the laboratory. Overall, we document shared experiences of respondents during the COVID-19 laboratory shutdown and identify areas of improvement in the event widespread laboratory closures occur in the future.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".