Bibliographic record
Abstract
We are always recruiting in rural. Even those ‘most envious of rural communities who surely have enough’ are recruiting (insert expletive uttered sotto voce). It’s a tiring lament. Why does it have to be so? There are many reasons but changing physician factors as mentioned in this issue’s study about recruiters (Page 141) partly explain it. New graduates increasingly require tailored approaches. It’s not that we have a job that they may want to do, but determining what life can we make for them (and their families). These important negotiations are not only necessary to attract someone but also ensure that the community keeps them. There are limits of course as there will be a set of community healthcare needs based on rural demographics and social determinants of health. Rural communities are ageing faster than urban ones. Our isolation, poverty and chronic disease-burden do not make attracting healthcare workers easy. In fact, the communities that need physicians the most appear to be the hardest for recruiters. However, even fortunate communities must recruit for the future as things can change in a moment by a single person. Fundamentally, rural suffers from quantum physics’ effects. Unlike urban the loss or gain of a single physician is state changing. One in five to one in four is a felt thing that cramps your intestines. Two Family Practice Anaesthetists who recruit a third is a landmark. Replacing patchy locum surgical coverage with a local surgeon is fireworks and champagne. Rural medical education is even couched overtly or covertly in terms of recruitment. If you take a student, you might be increasing your chances of recruiting a physician. To a nuanced extent, this is indeed true. However, to say that we are succeeding or not in Canada with the rural training of the upcoming rural medical workforce is probably premature to opine. For rural communities who are always recruiting, colour me sceptic.
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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.090 | 0.083 |
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".