The SOMERS Index: A simple instrument designed to predict the likelihood of rural career choice
Bibliographic record
Abstract
OBJECTIVE: The World Health Organization has drawn up a set of strategies to encourage health workers to live and work in remote and rural areas. A comprehensive instrument designed to evaluate the effectiveness of such programs has not yet been tested. Factors such as Stated rural intention, Optional rural training, Medical sub-specialization, Ease (or self-efficacy) and Rural Status have been used individually or in limited combinations. This paper examines the development, validity, structure and reliability of the easily-administered SOMERS Index. DESIGN: Limited literature review and cross-sectional cohort study. SETTING: Australian medical school. PARTICIPANTS: A total of 345 Australian undergraduate-entry medical students in years 1 to 4 of the 5-year course. MAIN OUTCOME MEASURES: Validity of the factors as predictors of rural career choice was sought in the international literature. Structure of the index was investigated through Principal Components Analysis and regression modelling. Cronbach's alpha was the test for reliability. RESULTS: The international literature strongly supported the validity of the components of the index. Factor analysis revealed a single, strong factor (eigenvalue: 2.78) explaining 56% of the variance. Multiple regression modelling revealed that each of the other variables contributed independently and strongly to Stated Rural Intent (semi-partial correlation coefficients range: 0.20-0.25). Cronbach's alpha was high at 0.78. CONCLUSIONS: This paper presents the reliability and validity of an index, which seeks to estimate the likelihood of rural career choice. The index might be useful in student selection, the allocation of rural undergraduate and postgraduate resources and the evaluation of programs designed to increase rural career choice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".