Creating knowledge management skills in primary care residents: a description of a new pathway to evidence-based practice in the community
Bibliographic record
Abstract
For several years, I have coordinated a critical appraisal course for residents. Participants, who were organised initially as a traditional journal club, would gather weekly and review an important research paper that either had recently been published or had been found during a Medline search while attempting to answer a question that arose during clinic. The housestaff consistently rated the course highly and appeared to be happy with it. When I ran into them after they were in practice, however, they would frequently reminisce fondly about “the days when we had time to read journals.” As for many educational interventions during residency, the traditional journal club seemed appropriate but unfortunately did not fit with “real world” practice. Several major factors influence the uptake of the practice of evidence-based medicine in primary care, including time constraints and the volume of clinical literature.1–4 However, several recent developments have made tackling these obstacles possible: high quality pre-appraised evidence resources; multi-intervention continuing education models that fit the learners' needs, setting, and social environment; and improved technology that makes delivering knowledge to the point of care possible. After considering these factors, I arrived at a conclusion similar to that of other evidence-based medicine proponents,5—that evidence-based clinicians of the future may …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".