Mentorship for the physician recruited from abroad to Canada for rural practice
Bibliographic record
Abstract
BACKGROUND: Mentoring is one way to help physicians new to a country assimilate. AIM: This study examined the feasibility and focus of a mentoring program from the perspective of medical leaders (leaders) and physicians new to Canada (physicians). METHODS: Focus groups with 23 physicians were held in six regional centers. Face-to-face interviews were held with 10 leaders. They were asked to discuss how a mentoring program might be helpful and how a program might be designed and evaluated. RESULTS: Both leaders and physicians recognized that mentorship would support the physician socially, professionally, and emotionally. They told us that mentorship programs should be structured carefully to build trust, allow mentors and mentees some selection, be in geographic proximity where possible, and have transparent rules. While leaders felt that evaluation would be an important part of a mentorship program, the physicians disagreed noting that it would change the nature of the program. Leaders stated that the ultimate evaluation of the program's success would be found in retention numbers. CONCLUSION: Physicians new to a country need support. Mentorship is a feasible approach to support new comers that may result in more efficient and effective integration, enculturation, and higher levels of retention.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".