The Clinical Research Associate Retention Study: A Report From the Children’s Oncology Group
Bibliographic record
Abstract
Pediatric medicine often struggles to receive adequate research funding for its small, yet vulnerable population of patients. Remarkable discovery in pediatric oncology is credited in large part to the collaborative structure of its research community. The Children's Oncology Group conducts studies supported by the National Cancer Institute. The clinical research associate (CRA) discipline comprises professionals who support administrative duties, regulatory duties, subject management, and data collection at individual research sites. The purpose of this study was to identify factors associated with CRA retention, as the group continues to have high turnover and position vacancy. A cross-sectional survey design was used to characterize the most frequently cited reasons CRAs gave when considering leaving or staying within their position. Results suggest that low salary, unmanageable workload, lack of career advancement and professional development, and lack of research commitment from the medical team were associated with intent to leave CRA positions. The most frequently cited reasons for staying at their job were the meaningfulness and interest in the work, a supportive principal investigator, and enjoyment working with colleagues. CRAs reported serious but eminently solvable issues that can be addressed using practical and low-cost solutions to improve job satisfaction and retention.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".