76 Advocating for less in primary care: PEER guidance
Bibliographic record
Abstract
PEER (Patients, Experience, Evidence, Research) is a group of family physicians and other primary care providers, partly support by the College of Family Physicians of Canada and it’s chapters, that provide clinical guidance to primary care free from industry influence. The group creates guidelines, patient decision aids, Tools for Practice (brief evidence-based summaries), presentations, podcasts, and performs original research. Much of the research, programs, tools and education has focused on advocacy for primary care and rational use of resources. This workshop will examine the considerable outreach and impact that PEER has had in primary care in Canada, with a specific focus on efforts to minimize interventions and enhance shared decision making. We will review our first Simplified PEER guideline, target discussions of risk with patients rather than surrogate markers or prescribing medications. We’ll review our decision aids and Simplified Chronic Pain Guideline promoting activity and counselling above pharmaceuticals. We’ll review 12 years of podcasts and Tools for Practice advocating reduction in x-rays, lab testing (like TSH), surrogate targets, ineffective medications and more. We have also written and presented on the opportunity cost of focusing on low yield activities in primary care. PEER is by primary care, for primary care, and through consistent hard-work, has become a recognized voice in Canada for rational, appropriate care promoting patient values.
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 | 0.023 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".