Bibliographic record
Abstract
Purpose: To measure the effect of including a clinical librarian in the health care team on medical residents and clinical clerks.Method: In 2009, medical residents and clinical clerks were preassigned to one of two patient care teams (intervention and control).Each team had a monthlong rotation on the general medicine teaching unit.The clinical librarian joined the intervention team for morning intake, clinical rounding, or an afternoon patient list review, providing immediate literature searches, formal group instruction, informal bedside teaching, and/or individual mentoring for use of preappraised resources and evidence-based medicine search techniques.Both intervention and control teams completed pre-and postsurveys comparing their confidence levels and awareness of resources as well as their self-reported use of evidence for making patient care decisions.The nonintervention team was surveyed as the control group.Results: The clinical librarian intervention had a significant positive effect on medical trainees' self-reported ability to independently locate and evaluate evidence resources to support patient care decisions.Notably, 30 of 34 (88%) reported having changed a treatment plan based on skills taught by the clinical librarian, and 27 of 34 (79%) changed a treatment plan based on the librarian's mediated search support.Conclusions: Clinical librarians on the care team led to positive effects on self-reported provider attitudes, provider information retrieval tendencies, and, notably, clinical decision making.Future research should evaluate economic effects of widespread implementation of on-site clinical librarians.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.209 | 0.081 |
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".