Physician assessments of the value of therapeutic information delivered via e-mail.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Although e-learning programs are popular and access to electronic knowledge resources has improved, raising awareness about updated therapeutic recommendations in practice continues to be a challenge. OBJECTIVE OF PROGRAM: To raise awareness about and document the use of therapeutic recommendations. PROGRAM DESCRIPTION: In 2010, family physicians evaluated e-Therapeutics (e-T) Highlights with a Web-based tool called the Information Assessment Method (IAM). The e-T Highlights consisted of information found in the primary care reference e-Therapeutics+. Each week, family physicians received an e-mail containing a link to 1 Highlight from a different chapter of e-Therapeutics+. Family physicians received continuing medical education credits for each Highlight they rated with the IAM. Of the 5346 participants, 85% of them were full-time or part-time practitioners. A total of 31 429 Highlights ratings were received in 2010 (median of 2 ratings per participant, range 1 to 49). Among participants who rated more than 2 Highlights, the median number of ratings was 7 (mean 11.9). The relevance of the information from individual Highlights varied widely; however, for 90% of the rated Highlights participants indicated total or partial relevance of the information for at least 1 patient. For 41% of rated Highlights, participants expected patient health benefits to result from implementing the recommendation, such as avoiding an unnecessary or inappropriate treatment, or a preventive intervention. CONCLUSION: This continuing medical education program stimulated family physicians to rate therapeutic recommendations that were delivered weekly via e-mail. The process of rating e-T Highlights with the IAM raised awareness about treatment recommendations and documented self-reported use of this information in practice.
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.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".