In the Eye of the Hurricane: Careers under Lockdown
Bibliographic record
Abstract
The COVID-19 pandemic has created career disruptions and shocks for many individuals, due to layoffs, reduced work hours and increased work–life conflict. Our study aimed to explore individual-level perceptions of people regarding their careers during the first lockdown in the UK, and to test potential implications of the situation for individuals’ career sustainability. For a deeper understanding of these perceptions, we used a sequential mixed-methods research design. First, we conducted a qualitative study, using semi-structured interviews to explore how people perceive their careers during early stages of the pandemic. We identified two themes that characterize the common experiences during this time period, namely employer support and careful optimism, that play an important role in the way careers unfold. Then, in the quantitative study, we conducted an online survey to empirically test a research model that links the concept of employer support with employability, career satisfaction and mental well-being. We also investigate the mediating role of career empowerment, which is a motivational cognitive construct that captures individual cognitions of agentic control over one’s career. Our research provides a rich snapshot that depicts people’s perceptions of careers during a shock event, which has both theoretical and practical implications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".