Moving Into Medical Practice in a New Community: The Transition Experience
Bibliographic record
Abstract
INTRODUCTION: Physicians undertake many transitions during the course of a medical career. The purpose of this study was to explore the experiences of physicians who moved to a new community. METHODS: A semistructured interview format was used to explore transitional experiences, including reasons for moving; the role of colleagues, learning, and organizational structures; how various mediating factors affected perceptions; and how the experience affected the physicians personally. We used qualitative methods in which data were collected, coded, and analyzed concurrently. RESULTS: 20 physicians from family medicine, internal medicine, and pediatrics described their experiences. Both the professional context and the geographic location affected physicians' perceptions of the move. Both internal and external mediating factors appeared to influence how physicians experienced and adjusted to the move. Physicians who joined functioning units appeared to have fewer problems. The physicians who had more difficulty were physicians who did not come to a specific job, often coming as the result of a spousal move; did not have a professional network in the city; had not sorted out licensure requirements; and were entering community (not institutional) practice. DISCUSSION: This study demonstrates the critical nature of institutional support structures to integrate the newcomer, collegial relationships within the workplace, and the importance of family and friends in mediating the adjustment period. Consideration should be given to structured mentorship or peer-buddy programs and longitudinal educational programs (eg, rounds) that may enable physicians to establish networks and gain practical local knowledge quickly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".