It begins with the right supervisor: Importance of mentorship and clinician-investigator trainee satisfaction levels in Canada
Bibliographic record
Abstract
PURPOSE: Clinician Investigator Trainee Association of Canada/ Association des cliniciens-chercheurs en formation du Canada (CITAC/ACCFC) represents the interests of clinician-investigator (CI) trainees across Canada. To better advocate for the successful training of CI trainees in Canada, CITAC/ACCFC conducted a survey to assess satisfaction with their training and to find what factors were most associated with satisfaction level. METHODS: A nominal scale-based psychometric survey was conducted online in 2009 on CI trainees in Canada (including MD/MSc, MD/PhD, or CIP/SSP). One hundred fifteen out of a target population of approximately 350-400 responded. Survey respondents were asked to rate their level of satisfaction in four areas: 1) quality of training, 2) financial support, 3) mentorship satisfaction and 4) program structure. Ratings in these four areas were also combined to produce a measure of overall satisfaction. RESULTS: At least half of the respondents were 'completely satisfied' in each of the four categories other than mentorship. While 98% of respondents considered mentorship as important to their success, 62% expressed some level of dissatisfaction with the level of mentorship received. Moreover, increased levels of mentorship were strongly associated with increased levels of overall satisfaction. CONCLUSION: The discrepancy between CI trainees' perceived importance of mentorship and the level of satisfaction in mentorship received reveals a strategic area where CI training should be improved. Recognizing that good mentorship in a CI training program often begins with one's research supervisor, the CITAC/ACCFC has compiled six specific recommendations for finding a good supervisor.
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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 | 0.037 |
| 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 | 0.008 |
| 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 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".