Factors Influencing Health Career Choices During Clinicians’ First Three Years in Practice
Bibliographic record
Abstract
Background: Health systems globally need more clinicians to work rurally and in community-based primary care. This study explores factors influencing health graduates’ choice of clinical setting and geographical location during early careers, across a range of disciplines that work together to support the health of people in community-based and rural locations.Methods: Students from eight disciplines (n = 611) were recruited prior to their final year of pre-registration training. Data were collected via three electronic surveys completed at the end of participants’ first, second, and third year of clinical practice. Data were managed and analyzed with Template Analysis.Findings: Similar factors influenced clinical setting and location choice but differed in relative importance for each. The nature of the job itself was the most important factor influencing clinical setting choices. A broader range of influences were important to geographical location choices including personal reasons, the nature of the job, the nature of the location, and job availability and opportunities. Regulatory or training requirements limited choices available to some clinicians, particularly those from medicine.Conclusion: A range of complex and interacting factors influenced health graduates’ career choices. Findings indicate that a broad system-wide approach is needed to address community and rural health workforce needs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".