Expected health benefits of e-Therapeutics Highlights according to pharmacists and physicians
Bibliographic record
Abstract
The purpose of this research brief is to describe the expected health benefits of e-Therapeutics Highlights,* as evaluated by Canadian pharmacists and family physicians. The Canadian Pharmacists Association (CPhA), the College of Family Physicians of Canada (CFPC) and the Information Technology Primary Care Research Group (McGill University) collaborated to create an innovative continuing education program, called e-Therapeutics Highlights. Highlights are key treatment recommendations from e-Therapeutics+.** CPhA produces and maintains e-Therapeutics+, a web portal comprising clinical topic summaries that are authored and peer reviewed by subject matter experts (www.e-therapeutics.ca). Once a week, CPhA and CFPC members receive an e-Therapeutics Highlight delivered by e-mail, which they have the option of evaluating by means of a reflective learning activity based on the Information Assessment Method (IAM).1 For each rated Highlight, pharmacists receive continuing education units, and family physicians receive Mainpro credits. We present the aggregated results of IAM ratings as a way of summarizing the wisdom of the crowd of Highlight raters. Crowdsourcing is a force multiplier, defined as the release of online material to a crowd of users who may be interested in contributing ideas or performing a task such as rating a Highlight (voting) and submitting their work to a platform or an organization such as the CPhA, for the profit of the entire community.2,3 Large groups can be collectively wise in identifying relevant information.4 This principle of “crowdsourcing” has been applied to the development of innovative learning network approaches such as those seen in Wikipedia and various voting systems.5,6 Crowdsourcing allows the traditional “ask-the-user” approach to reach a wider audience.7,8
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".