Bibliographic record
Abstract
For most of us, our conscious first contact with the medical community and healthcare system is through family medicine.Whether we are located within the heart of a vibrant city or in a peaceful rural community, family medicine is universally important.In health or disease, family medicine practitioners are present and actively involved in serving their patients, from birth through old age, making this one of the few specialties to establish such long-lasting and enduring doctor-patient relationships.While the unmet and growing demands for access to family medicine care are ubiquitous, 1 this unique area of practice encompasses over 47,000 practicing physicians in Canada, 2 highlighting its significance in our healthcare ecosystem.In fact, increasing the number of family physicians per capita has been directly linked with measurable benefits in longevity and health outcomes.3 As such, this area of practice is indisputably critical for the well-being of the Canadian population.Arguably, a defining feature of family medicine is its breadth and scope of practice.In recognizing this, the topics covered in the current issue reflect this reality.We hope that this issue echoes the current challenges and exciting areas of development in family medicine.Here is an overview of what you can expect in this current issue:
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.047 | 0.020 |
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".