A National Survey of Mentoring Practices for Young Investigators in Circulatory and Respiratory Health
Bibliographic record
Abstract
Background. Improving mentorship may help decrease the shortage of young investigators (graduate students, postdoctoral fellows, and new investigators) available to work as independent researchers in cardiovascular and respiratory health. Objectives. To determine (1) the mentoring practices for trainees affiliated with the Canadian Institutes of Health Research (CIHR), Institute of Circulatory and Respiratory Health (ICRH), (2) the positive attributes of mentors, and (3) the recommendations regarding what makes good mentorship. Methods. We conducted a survey and descriptive analysis of young investigators with a CIHR Training and Salary Award from 2010 to 2013 or who submitted an abstract to the ICRH 2014 Young Investigators Forum. Clinicians were compared to nonclinicians. Results. Of 172 participants, 7.0% had no mentor. Only 43.6% had defined goals and 40.7% had defined timelines, while 54.1% had informal forms of mentorship. A significant proportion (33.1%) felt that their current mentorship did not meet their needs. Among clinicians, 22.2% would not have chosen the same mentor again versus 11.4% of nonclinicians. All participants favored mentors who provided guidance on career and work-life balance. Suggestions for improved mentoring included formal mentorship, increased networking, and quality assurance. Conclusion. There is an important need to improve mentoring in cardiovascular and respiratory health.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".