Canadian research fellowship training programs in digestive sciences: achievements and challenges.
Bibliographic record
Abstract
BACKGROUND: The Canadian Association of Gastroenterology (CAG) is committed to fostering the development of future Canadian investigators. Up to 1986, research fellowship support was obtained from the Medical Research Council (MRC) of Canada. Since that time, several peer-reviewed, industry-sponsored, CAG-supported research fellowships and a variety of independently funded awards have augmented this effort. In the same period, peer-reviewed operating grants (OGs) from the MRC and other agencies have been constrained. The aim of this study was to determine the success of CAG, MRC or any other Canadian research fellowships in the development of career investigators in digestive sciences and to identify factors influencing the outcomes of such training. METHODS: MRC records and the minutes of CAG annual meetings were reviewed to identify research fellowship support. Canadian program directors were requested to list research fellows affiliated with their groups between 1986 and 1997. Only fellowships providing at least 1 year of training were included. A 7-page questionnaire detailing biographic characteristics, the site and duration, and specific issues related to the quality of research training was sent to identified trainees. Significant associations between success in achieving an academic appointment or OG support and several variables of training were identified. RESULTS: Eighty-six research fellows were trained. Responses were obtained from 43 of them. The demographic characteristics of the whole group and the respondents were similar. Of the respondents, 81% of trainees obtained academic appointments. Fellowships longer than 1 year were associated with higher rates of academic posting, and MRC-funded fellows had greater success rates of academic appointments. Of eligible trainees 63% have obtained OG support. None of the other variables examined predicted success. Of the trainees responding, 85% valued the fellowship very highly. CONCLUSIONS: The establishment of the additional research fellowships has fostered the development of career investigators in digestive sciences. The high success rate of former trainees in obtaining academic appointments and OG support suggests that the fellowship programs are effective and appropriately oriented. The structure of the current programs does not require substantial revision. OG support for new investigators appears now to lag substantially.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".