Will this benefit my patients? Expected benefits of information from a continuing medical education program may lead to higher participation rates by family physicians
Bibliographic record
Abstract
In this commentary, we will describe our study and report results that will be of interest to information and education professionals and researchers. Evidence-based medicine requires health professionals to keep up to date with new research-based knowledge. Canadian physicians must now participate in Continuing Medical Education (CME) activities. CME strives to improve clinician performance as well as patient health outcomes. Our study was aimed to assess whether physicians who participated in a CME program and expected health benefits for their patients following an elearning activity were more likely to have higher participation in the program in subsequent years. Weekly treatment Highlights were delivered by email to practicing family physicians across Canada, who rated them using the Information Assessment Method (IAM). The number of expected benefits for patients reported by participants during 2016 was plotted against the number of instances of participation in 2017. Results show that the number of expected benefits in 2016 was correlated with the number of IAM ratings in 2017.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".