Influence of participant and reviewer characteristics in application scores for a hematology research training program
Bibliographic record
Abstract
The American Society of Hematology Clinical Research Training Institute (CRTI) is a clinical research training program with a competitive application process. The objectives were to compare application scores based on applicant and reviewer sex and underrepresented minority (URM) status. We included applications to CRTI from 2003 to 2019. The application scores were transformed into a scale from 0 to 100 (100 was the strongest). The factors considered were applicant and reviewer sex and URM status. We evaluated whether there was an interaction between the characteristics and time related to application scores. In total, 713 applicants and 2106 reviews were included. There was no significant difference in scores according to applicant sex. URM applicants had significantly worse scores than non-URM applicants (mean [standard error] 67.9 [1.56] vs 71.4 [0.63]; P = .0355). There were significant interactions between reviewer sex and time (P = .0030) and reviewer URM status and time (P = .0424); thus, results were stratified by time. For the 2 earlier time periods, male reviewers gave significantly worse scores than did female reviewers; this difference did not persist for the most recent time period. The URM reviewers did not give significantly different scores across time periods. URM applicants received significantly lower scores than non-URM applicants. The impact of reviewer sex and URM status changed over time. Although male reviewers gave lower scores in the early periods, this effect did not persist in the late period. Efforts are required to mitigate the impact of applicant URM status on application scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".