Results of a mixed-methods study on barriers to physician recruitment in Newfoundland and Labrador
Bibliographic record
Abstract
Background: Like many rural and remote parts of Canada, the province of Newfoundland and Labrador (NL) struggles to maintain a skilled healthcare workforce. As many as 20% of people in the province are thought to be without a primary care physician. The purpose of this study was to determine the barriers recent Memorial University of Newfoundland medical alumni have faced in establishing medical practice in NL. Methods: An online survey followed by question-standardised focus group sessions. Results: Two hundred and ninety-one physicians who graduated from Memorial University of Newfoundland medical school between the years of 2003 and 2018 completed the survey. Nearly 80% of respondents recalled that NL was their preferred practice location at some point during training: 79.4% (n = 231) at the beginning of medical school and 77.7% (n = 226) at the beginning of residency training. However, at the time of the survey, only 160 (55.0%) respondents were working in NL. Respondents reported significant cultural and systemic barriers in trying to work in NL, including ineffective recruitment offices, lack of transparency in communication with health authorities, inequitable distribution of resources and workloads, lack of appropriate resources to support new positions, and return-of-service agreements that are not honoured or followed-up. Conclusion: Our study outlines a number of ways in which recruitment and retention could be improved, ultimately improving provincial health care and helping to fulfil the mandate of the medical school.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".