Bibliographic record
Abstract
I have returned rejuvenated from our annual Rural and Remote Medicine Conference in Edmonton in April. I attended my first conference as a resident in 2006 and I have not missed one since (except for the one we all missed in 2020). Over the last 18 conferences, I have seen the organization grow in scope and impact on rural medicine. I was overjoyed to hear we hosted our largest conference ever this year with over 1100 registrants from across our country and beyond. My clinic partner Dr. Jared Van Bussel and my friend Dr. Kyle Sue-Milne hosted this fantastic gathering which always feels like coming home. I give full credit to our COO, Jennifer Barr, and all the SRPC staff on the amazing work as always. Advocating for rural generalism has never been more needed. The number of rural Canadians without a physician is unacceptable. Rural emergency room closures have become more and more commonplace. Rural obstetrics is sadly on the decline; many rural women are being ignored by health and training systems and denied the opportunity to deliver in their home communities. The challenges are indeed great, and I believe that the SRPC is well-positioned to make a significant impact in healing what ails rural healthcare. There is hope on the horizon. In Alberta, work is underway to establish two new distributed medical campuses in the hopes of training more rural generalists. Across the country, four other new medical schools (SFU, UPEI, TMU, and York) are beginning to take shape, all with a particular focus on primary care. This is where we need your help. These new institutions will need preceptors, curriculum experts, and clinical leaders to make these dreams a reality. I know our plates are more than full, especially after the COVID-19 pandemic, but our voices need to be heard at these tables. Your voice also needs to be heard by the SRPC. If you are not a member, please consider joining. If your colleagues are not members, remind them of the great work we have done towards national licensure, supporting hundreds of physicians through the National Advanced Skills Training Program, and our relentless advocacy that led the CFPC to cease the implementation of the third-year in family medicine residency training. I cannot thank Dr. Sarah Lesperance enough for her tireless work as President for the past 2 years and the sage council I have already received from her in her new role as Past President. Watching Sarah grow our organization to new heights-all while dedicating herself to her patients and family-has been inspirational. I am honoured to represent the organization in my new role as President and look forward to connecting with you all.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.123 | 0.061 |
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".