Impact of the International Collaborative Addiction Medicine Research Fellowship on Physicians’ Future Engagement in Addiction Research
Bibliographic record
Abstract
Background: To evaluate how an international one-year intensive research training program for addiction medicine physicians contributed to subsequent research involvement and productivity. Methods: We prospectively compared addiction medicine physician fellows admitted to a one-year training program with non-admitted controls, using baseline questionnaire and peer-reviewed publication data. Participants’ publication activity was assessed from fellowship application date onwards using biomedical databases (e.g., PubMed, Embase). Results: Between July 2014 and June 2020, which is six years of cohorts, 56 (39 women) physicians, both fellows ( n = 25) and non-admitted applicants ( n = 31), were observed and included in the study, contributing 261 person-years of observation. At baseline, in the fellows’ cohort: 76% of participants (19/25) reported past research involvement, 24% (6/25) had one or more advanced graduate degrees (e.g., MPH), and the median number of peer-reviewed, first author publications was one (Interquartile Range [IQR] = 0–2). At baseline, in the controls’ cohort: 84% of participants (26/31) reported past research involvement, 39% (12/31) had one or more advanced graduate degrees, and the median number of peer-reviewed, first author publications was zero. The physicians’ training included internal medicine ( n = 8), family medicine ( n = 33), psychiatry ( n = 5) and others ( n = 4). At follow up, there was a significant difference between fellows ( n = 25) and controls ( n = 31) in total number of publications (Rate Ratio [RR] = 13.09, 95% Confidence Interval [CI], 5.01 − 34.21, p < 0.001), as well as first author publications (RR = 5.59, 95% CI, 2.23 − 14.06, p < 0.001). Conclusion: In the six-year observation period, fellows’ productivity indicates undertaking this fellowship was associated with significant research outputs in comparison to controls, signaling successful training of addiction physicians to help recruit addiction medicine physicians to participate in addiction research.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".