Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to determine whether physician-scientists in psychiatry in Canada are in decline, as was reported for medicine overall during the 1990s in the United States. DESIGN: Federal databases were searched to study grant applications in the area of mental health submitted by physician-scientists compared with PhD-scientists for the period 1985-2001. A survey of Canadian Residency Training Program Directors was carried out for the graduating class of 2000. SETTING: The Canadian publicly funded university system. PARTICIPANTS: Applicants to the Medical Research Council of Canada and its successor, the Canadian Institutes of Health Research, for operating grant support and Residency Training Program Directors. INTERVENTIONS: None. OUTCOME MEASURES: Comparison over time between MD and PhD applicants regarding the number of grant applications submitted, the proportion of applications funded and the number of new applications submitted, with separation of applications submitted to a predominantly "biomedical" peer review committee and to a predominantly "clinical research" peer review committee. The survey obtained information about a number of variables related to research training. RESULTS: The situation for physician-scientists in psychiatry in Canada appeared remarkably similar to general findings in US studies. Relative to PhD applicants, fewer grant proposals were being made by physicians (paired t16 = 7.08, p < 0.001) and, in consequence, fewer proposals were funded. The proportion of proposals funded was similar for MD and PhD applicants (paired t16 = 0.27, p = 0.79). Grant applications made to the predominantly biomedical committee were more likely to be funded than applications to the committee with an orientation toward clinical research (paired t7 = 5.53, p < 0.001). Applications by PhD-scientists to the biomedical committee showed the largest increase over time and were the most successful. From the survey of graduating classes, close to one-third of residents had authored or co-authored a publication during residency. Only 7% were proceeding to research fellowship training. The remuneration available for fellowship training was about one-third of what graduating classmates could expect to earn in the first year of practice. CONCLUSIONS: Quantitative data indicate that physician-scientists in psychiatry in Canada are experiencing the same pressures and challenges as physician-scientists in the United States. A plan of action tailored to the needs of the psychiatric community in Canada needs to be developed.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".