Challenges for rural communities in recruiting and retaining physicians: a fictional tale helps examine the issues.
Bibliographic record
Abstract
n the 2003 Qubcois movie Seducing Dr Lewis, 1 the island of Sainte-Marie-la-Mauderne in Quebec is in decline, with many of its residents unemployed.A company offers the island's citizens hope for a more prosperous future when it considers building a factory on the island; however, there is one catch: the community must have a permanent family physician.The community's recruitment attempts for a physician have failed for 15 years.Then, through nefarious means, a young doctor, Christopher Lewis, is exiled to the island for a month.Now the community's only hope is to seduce Dr Lewis to stay.This movie offers a number of issues for discussion about the recruitment and retention of family physicians to rural and remote areas.In this commentary, we use the movie to present some of the challenges for rural communities during these processes.The literature on this topic often highlights the perspectives of physicians but not those of communities.It is important to consider the perspectives of communities, as research establishes that they are key to determining why physicians choose some places to practise over others. [2][3]3][4][5][6] Additionally, as some physicians participate in community recruitment processes, it might be helpful to understand the viewpoints of communities in more depth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".