Exploring medically-related Canadian summer student research programs: a National Cross-sectional Survey Study
Bibliographic record
Abstract
Abstract Background Summer student research programs (SSRPs) serve to generate student interest in research and a clinician-scientist career path. This study sought to understand the composition of existing medically-related Canadian SSRPs, describe the current selection, education and evaluation practices and highlight opportunities for improvement. Methods A cross-sectional survey study among English-language-based medically-related Canadian SSRPs for undergraduate and medical students was conducted. Programs were systematically identified through academic and/or institutional websites. The survey, administered between June–August 2016, collected information on program demographics, competition, selection, student experience, and program self-evaluation. Results Forty-six of 91 (50.5%) identified programs responded. These SSRPs collectively offered 1842 positions with a mean 3.76 applicants per placement. Most programs (78.3%, n = 36/46) required students to independently secure a research supervisor. A formal curriculum existed among 61.4% (n = 27/44) of programs. Few programs (5.9%, n = 2/34) offered an integrated clinical observership. Regarding evaluation, 11.4% (n = 5/44) of programs tracked subsequent research productivity and 27.5% (n = 11/40) conducted long-term impact assessments. Conclusions Canadian SSRPs are highly competitive with the responsibility of selection primarily with the individual research supervisor rather than a centralized committee. Most programs offered students opportunities to develop both research and communication skills. Presently, the majority of programs do not have a sufficient evaluation component. These findings indicate that SSRPs may benefit from refinement of selection processes and more robust evaluation of their utility. To address this challenge, the authors describe a logic model that provides a set of core outcomes which can be applied as a framework to guide program evaluation of SSRPs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".