Researching researchers: exploring the challenges of conducting research during a pandemic
Bibliographic record
Abstract
BACKGROUND: Research assistants (RAs) are vital for the successful completion of research. When data collection and recruitment are disrupted, like during the COVID-19 pandemic and accompanying restrictions, the effects on RAs attempting to conduct research are unclear. PARTICIPANTS AND PROCEDURE: could help patients uphold health and safety procedures during the COVID-19 pandemic participated in semi-structured interviews. RESULTS: Thematic analysis of the interview data identified four key themes (and sub-themes) that reflected RAs' experiences of conducting research during the COVID-19 pandemic: inspiration and motivation; research barriers; human connections and relationships; and creativity and problem-solving. The first theme focused on the sources of RAs' inspiration and motivation to participate in research; the second focused on the barriers that affected data collection and recruitment. The third theme described the impact that human connections and relationships had on the success of the research, and the final theme explored the RAs' creativity and problem-solving approaches, which aided in navigating the challenges faced during the pandemic. The RAs overcame the challenges with positive attitudes, creativity, and collaboration. CONCLUSIONS: Overall, the results reveal how the RAs explored creative strategies to adapt research methods to suit unanticipated circumstances and develop interpersonal skills to facilitate participation in future research and career activities.
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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 | 0.256 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.035 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".