When the labs closed: graduate students’ and postdoctoral fellows’ experiences of disrupted research during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Physical distancing 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 mixed-method analysis of survey and interview data to identify shared experiences, feelings, concerns, and supports.Respondents’ emotions reported from different points during the COVID-19 laboratory closures align with previously described Kübler-Ross model of grief following change. Respondents describe closure processes as chaotic and confusing, primarily due to inconsistent communication between graduate students, postdoctoral fellows, supervisors, and administration. Respondents reported increased anxiety during closures, however, those who experienced uniform laboratory closures also developed a sense of solidarity and safety while those with staggered shutdowns did not. Graduate students and postdoctoral fellows reported increased indications of distress while working from home. Four key barriers to working from home were described; technical issues, domestic distractions, decreased motivation, and poor mental health. Five key supports while working at home were also identified, including financial security, social connections, establishing and maintaining routines, mental health support, and support from supervisors and administration. Respondents had concerns of how COVID-19 might impact them, including decreasing competitiveness applicants while limiting future employment opportunities. Finally, we outline five types of supports which can be implemented by supervisors and administrators to support graduate students and postdoctoral fellows to return to the laboratory, including; personal protective equipment and protocols, understanding and empathy, guidance and direction, timeline support, and financial support. 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".