The impact of the COVID-19 pandemic on early career researcher activity, development, career, and well-being: the state of the art
Bibliographic record
Abstract
Purpose This paper aims to identify the documented effects of the COVID-19 pandemic on early career researcher (ECR) activity, development, career prospects and well-being. Design/methodology/approach This is a systematic literature review of English language peer-reviewed studies published between 2020 and 2021, which provided empirical evidence of the impact of the pandemic on ECR activity and development. The search strategy involved online databases (Scopus, Web of Science and Overton); well-established higher education journals (based on Scopus classification) and references in the retained articles (snowballing). The final sample included 11 papers. Findings The evidence shows that ECRs have been affected in terms of research activity, researcher development, career prospects and well-being. Although many negative consequences were identified, some promising learning practices have arisen; however, these opportunities were not always fully realised. The results raise questions about differential effects across fields and possible long-term consequences where some fields and some scholars may be worse off due to priorities established as societies struggle to recover. Practical implications There is a need for revised institutional and national policies to ensure that sufficient measures are implemented to support ECRs’ research work in a situation where new duties and chores were added during the pandemic. Originality/value This paper provides insights into the impacts of the initial societal challenges of the pandemic on ECRs across disciplines that may have long-lasting effects on their academic development and well-being.
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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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".