How do we teach clinicians where the resources for best evidence are?
Bibliographic record
Abstract
The Sinai Health System (SHS) Library created an online tool kit that groups electronic resources into tiers based on the hierarchy of evidence, in a step-by-step approach. Mobile application options are available for most of the resources. The goal is to provide a simple, practical teaching tool to help clinicians easily find quality health information from the vast offerings of publishers. Since its publication in 2008, the original tool kit received positive feedback from medical students and in-house clinical staff. As well, the tool kit has been incorporated into the teachings of the Royal College of Surgeons and Physicians of Ontario, Ministry of Public Health, and various hospital and patient libraries across the Greater Toronto Area. The SHS Library encourages other libraries and institutions to adapt the tool kit for their users. In the future, this tool kit will be revised to tailor to the research needs of nursing and allied health staff.
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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 | 0.098 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.025 | 0.042 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".