Collaborative biostatistics and epidemiology in academic medical centres: A survey to assess relationships with health researchers and ethical implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The role of collaborative biostatisticians and epidemiologists in academic medical centres and how their degree type, supervisor type and sex influences recognition and feelings of respect is poorly understood. We conducted a cross‐sectional survey of self‐identified biostatisticians and epidemiologists working in academic medical centres in the United States or Canada. The survey was sent to 341 contacts at 125 institutions who were asked to forward the survey invitation to faculty and staff at their institution and posted on Community sections of the American Statistical Association website. Participants were asked a variety of questions including if they felt pressured to produce specific results, whether they had intellectual and ethical freedom to pursue appropriate use of statistical methods in collaborative research and if they felt their contributions were appropriately recognized by collaborators. We received responses from 314 biostatisticians or related methodologists. A majority were female (59%), had a doctorate degree (52%) and reported to a statistician or biostatistician supervisor (69%). Overall, most participants felt valued by their collaborators, but that they did not have sufficient calendar time to meet deadlines. Doctoral‐level participants reported more autonomy in their collaborations than master's level participants. Females were less likely to feel recognized and respected compared with males. The survey results suggest that while most respondents felt valued by their collaborators, they have too many projects and need more time to critically review research. Further research is needed to understand why response differs by sex and how these responses fluctuate over time.
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.
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it