9. Innovation in research instruction: Pilot testing of team learning to promote peer reviewed grantwriting by clinician trainees
Bibliographic record
Abstract
We piloted the combination of Team-Based Learning (TBL) with interactive in-class instruction in grantsmanship to test its effectiveness in preparing clinician trainees to produce a national (CIHR) peer reviewed operating grant in a small class setting. The approach was integrated into a university graduate quantitative method and design course delivered to five trainees in 12 weekly 4hr sessions. Outcomes to assess knowledge acquisition, retention and application included percentage scores in seven TBL individual and student team tests (each containing 15-20 multiple-choice items), student participation in a mock peer review and student own submission of an operating grant (using CIHR peer review evaluation criteria to assess methodological coherency and soundness of the research design and plan, feasibility, relevancy and innovativeness). Also assessed were student perception of the approach on their learning (7 item questionnaire) and two peer teaching evaluations. In seven consecutive testing sessions, percentage scores for the individual tests were 80, 72, 76, 71, 83, 75 and 80 and corresponding team scores were 96, 96, 83, 100, 95, 97, and 97 suggesting an 18% increase in individual knowledge with team testing. Overall, student achievements were 93% for mock peer review and 78% for grant production. Trainees rated TBL and the interactive in-class activities as effective in consolidating knowledge and promoting complex research design decision making. Evaluations of the teaching were 4.7 and 4.8 out of 5. Findings suggest students mastered course content, that team learning increased individual knowledge and that trainees linked theory to successfully produce a CIHR operating grant. These pilot findings call for larger prospective studies to test the combined approach in larger classes and with other populations of clinician trainees. The clinician scientist: yesterday, today and tomorrow. Canadian Institutes of Health Research. http://www.cihr-irsc.gc.ca/e/pdf_22084.htm. Accessed February 25, 2007. Ringel SP. Steiner JF, Vickrey BG, Spencer SS. Training clinical researchers in neurology: We must do better. Neurology 2001; 57:388-392. Haidet P, O’Malley KJ, Boyd R. An initial experience with “Team Learning’ in medical education. Academic Medicine 2002; 77: 40-41.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".