Recruitment and retention of healthcare professionals in rural Canada: A systematic review
Bibliographic record
Abstract
INTRODUCTION: This review explores a pertinent issue for healthcare professionals and recruiters alike: which factors are most important in the recruitment and retention of these professionals in rural practice in Canada. Existing research concentrates on specific factors or focused populations. This review was created to explore multiple factors and a wider population of healthcare professionals, including chiropractors, osteopaths, dentists and physiotherapists. METHODS: A literature search was carried out on four databases. Data from included studies were extracted, and thematic analysis was conducted on relevant findings. The quality of individual studies was assessed, and then themes were evaluated for overall confidence based on four components, using the Confidence in the Evidence for Reviews of Qualitative Research. RESULTS: One quantitative and four qualitative articles were identified, all of which targeted physicians. Five themes - Personal/family matters, Community factors, Professional practice factors, Professional education factors and Economic factors - were generated in two domains, recruitment and retention. Forty major codes were generated through axial coding of open codes. Codes included attraction to rural lifestyle, recreational activities, Scope of practice, rural training and incentives. Scope of practice was deemed very important as a factor of recruitment, as was attraction to rural lifestyle. Incentives were found to be of little importance in influencing the recruitment of healthcare professionals, and even less important for retention. CONCLUSION: Wide scope of practice and attraction to the rural lifestyle were considered the most important for recruitment and to a lesser extent, retention, among the five papers studied. A lack of research was determined in the realm of factors influencing the recruitment and retention in healthcare professionals other than medical doctors in Canada. Therefore, it is recommended that further such studies investigate specific healthcare professionals.
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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 | 0.024 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".