Will this benefit my patients? Expected benefits of information from a continuing medical education program may lead to higher participation rates by family physicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it