Crowdsourcing trainees in a living systematic review provided valuable experiential learning opportunities: a mixed-methods study
Bibliographic record
Abstract
OBJECTIVES: To understand trainee experiences of participating in a living systematic review (LSR) for rheumatoid arthritis and the potential benefits in terms of experiential evidence-based medicine (EBM) education. STUDY DESIGN AND SETTING: We conducted a mixed-methods study with trainees who participated in the LSR and who were recruited broadly from training programs in two countries. Trainees received task-specific training and completed one or more tasks in the review: assessing article eligibility, data extraction, and quality assessment. Trainees completed a survey followed by a one-on-one interview. Data were triangulated to produce broad themes. RESULTS: Twenty one trainees, most of whom had a little prior experience with systematic reviews, reported a positive overall experience. Key benefits included learning opportunities, task segmentation (ability to focus on a single task, as opposed to an entire review), working in a supportive environment, international collaboration, and incentives such as authorship or acknowledgment. Trainees reported improvement in their competency as a Scholar, Collaborator, Leader, and Medical Expert. Challenges included communication and technical difficulties and appropriate matching of tasks to trainee skillsets. CONCLUSION: Participating in an LSR provided benefits to a wide range of trainees and may provide an opportunity for experiential EBM training, while helping LSR sustainability.
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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 | 0.105 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".