Bibliographic record
Abstract
medicine Canada is experiencing a shortageof family physicians. Bruce Wright and associates1 have identified several factors that might be used to increase the number of medical stu-dents choosing to enter family medi-cine. For its part, the Professional As-sociation of Internes and Residents of Ontario recently developed a position paper on the sustainability of family medicine, offering a view of this crisis from the perspective of Ontario’s new physicians.2 As our position paper states,1 we be-lieve that the lack of interest in family medicine initially develops during training, for a variety of reasons, in-cluding a lack of exposure to effective teamwork between family physicians and specialists. In addition, the position paper identifies many societal factors that have influenced the decline in sup-ply of family physicians in Canada. All of these factors will require short-term interventions married with a long-term strategy. Positive exposure to family practice in medical school and clerkship can spark new interest in the field, as well as reinforcing existing interest. For such exposure to occur, a cadre of dy-namic and enthusiastic family physi-cians is required both within medical schools and in community practice. The concerns of practising family physicians, including reasonable workloads and appropriate payment mechanisms, must be addressed to make such student experiences possi-ble, and new family physicians must be encouraged to undertake a mentor-ship role and must be supported in fulfilling that role.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.336 | 0.125 |
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".