Effectiveness of financial incentives in exchange for rural and underserviced area return-of-service commitments: systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of programs that provide financial incentives to physicians in exchange for a rural or underserviced area return-of-service (ROS) commitment. METHODS: Medline and Ovid HealthSTAR databases were searched from 1966 to 2002. STUDY SELECTION: The initial search yielded 516 results. Bibliography review yielded additional references. Articles were excluded if they involved financial incentives to change physician behaviours or enhance profit. Ten publications were selected as the highest level of evidence available. The quality of the evidence was low and of limited applicability (1 retrospective and 1 prospective cohort study, the remainder cross-sectional surveys). Three studies were from Canada, 1 from New Zealand, and the remaining 6 were from the United States. RESULTS: Outcome measures included initial recruitment of physicians, buyout rates and long-term retention. The majority of studies reported effective recruitment despite high buyout rates in some US-based programs. Increasing Canadian tuition and debt among medical students may make these programs attractive. The 1 prospective cohort study on retention showed that physicians who chose voluntarily to go to a rural area were far more likely to stay long term than those who located there as an ROS commitment. Multidimensional programs appeared to be more successful than those relying on financial incentives alone. CONCLUSION: ROS programs to rural and underserviced areas have achieved their primary goal of short-term recruitment but have had less success with long-term retention. Additional research is needed to examine the cost effectiveness of existing ROS programs and the incorporation of other retention strategies, such as medical education initiatives, community and professional support, differential rural fees and alternate funding models.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".