Approaches to locum physician recruitment and retention: a systematic review
Bibliographic record
Abstract
A robust workforce of locum tenens (LT) physicians is imperative for health service stability. A systematic review was conducted to synthesize current evidence on the strategies used to facilitate the recruitment and retention of LT physicians. English articles up to October 2023 across five databases were sourced. Original studies focusing on recruitment and retention of LT's were included. An inductive content analysis was performed to identify strategies used to facilitate LT recruitment and retention. A separate grey literature review was conducted from June-July 2023. 12 studies were retained. Over half (58%) of studies were conducted in North America. Main strategies for facilitating LT recruitment and retention included financial incentives (83%), education and career factors (67%), personal facilitators (67%), clinical support and mentorship (33%), and familial considerations (25%). Identified subthemes were desire for flexible contracts (58%), increased income (33%), practice scouting (33%), and transitional employment needs (33%). Most (67%) studies reported deterrents to locum work, with professional isolation (42%) as the primary deterrent-related subtheme. Grey literature suggested national physician licensure could enhance license portability, thereby increasing the mobility of physicians across regions. Organizations employ five main LT recruitment facilitators and operationalize these in a variety of ways. Though these may be incumbent on local resources, the effectiveness of these approaches has not been evaluated. Consequently, future research should assess LT the efficacy of recruitment and retention facilitators. Notably, the majority of identified LT deterrents may be mitigated by modifying contextual factors such as improved onboarding practices.
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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 | 0.033 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".