Fostering mentorship for clinician-investigator trainees: overview and recommendations
Bibliographic record
Abstract
PURPOSE: The Clinician Investigator Trainee Association of Canada/ Association des cliniciens-chercheurs en formation du Canada (CITAC/ACCFC) recently published the first survey to assess factors contributing to trainee satisfaction. One key finding is that increased level of mentorship strongly correlates with overall satisfaction; however, while 98% of respondents reported mentorship as important to success, more than 60% expressed some dissatisfaction with the mentorship received. To help address this discrepancy, we reviewed mentorship in academic medicine, focusing on clinician-investigator trainees, and distilled a set of recommendations for mentors, mentees and institutions. SOURCE: OVID and manual curation based on the search terms 'mentorship' AND 'education, medical and research' identified 198 articles. Two authours independently reviewed both titles and abstracts and narrowed them down to 75 articles, based on relevance to mentorship in academic medicine. Consensus resulted in the selection of 19 articles for detailed review. Principal findings and Conclusion: Mentorship is beneficial at each training stage and is associated with greater research productivity, career retention and promotion. Nevertheless, more rigorous studies are needed, especially regarding cost-effectiveness. Studies have identified the characteristics of good mentors, including the ability to ensure open communication, ability to maintain confidentiality and ability to ensure that there is no mentor-mentee competition. Similarly, the characteristics of good mentees have been identified as the ability to take ownership of a project and the ability to build a network or team of mentors. The literature has also identified the actions that institutions can take to facilitate mentorship, which include mentor training and recognizing mentorship through awards.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| 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".