Impact of the <scp>COVID</scp>‐19 Pandemic on Early Career Investigators in Rheumatology: Recommendations to Address Challenges to Early Research Careers
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has impacted the careers of trainees and early career investigators (ECIs). We sought to assess how the American College of Rheumatology (ACR) and the Rheumatology Research Foundation (RRF) can address the needs of those pursuing research careers. METHODS: The Committee on Research created a survey to assess the impact of COVID-19 and identify topics for the ACR and the RRF to address. In fall of 2020, we surveyed postdoctoral trainees and ECIs within 9 years of terminal training. Responses were analyzed using descriptive statistics and qualitative content analysis. RESULTS: Twenty-one percent of invitees responded to the survey (n = 365); of these, 60% were pursuing careers in academic research. Seventy-five percent of respondents in academic research career paths placed their primary projects on hold during the pandemic. The number of individuals pursuing a research career from 2020 to 2021 decreased by 5%. Respondents reported funding, caregiving, and lack of preliminary data as significant challenges. Suggested impactful interventions included increased funding, funding process reform, and expanding mentoring and networking resources. CONCLUSION: Major stressors identified during the pandemic included increased caregiving responsibilities and difficulty obtaining data and funding, for which respondents suggested increases and changes in funding programs as well as more mentoring and networking opportunities. Based on these, the Committee on Research proposes 3 priorities: 1) flexible funding mechanisms for ECIs and additional support for those impacted by caregiving; 2) virtual and in-person programs for career development and networking; and 3) curated content relevant to building a research career available on demand.
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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 | 0.116 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".