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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".