Bibliographic record
Abstract
Mentoring is routinely used as a tool to facilitate acquisition of skills by new professionals in fields like medicine, nursing, surgery, and business. While mentoring has been proposed as an effective strategy for knowledge and skills transfer in biostatistics and related fields, there is still much to be done to facilitate adoption by stakeholders, including academia and employers of biostatisticians. This is especially troubling given that biostatisticians play a key role in the success or otherwise of clinical research conducted for evidence-based decisions. In this paper, we offer suggestions on how mentoring can be applied in practice to advance the statistical training of future biostatisticians. In particular, we propose steps that academic statistics departments, professional statistical societies, and statistics organizations can take to advance the mentoring of young biostatisticians. Our suggestions also cover what mentors and mentees can do to facilitate a successful mentoring relationship.
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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 | 0.169 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.030 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".