Establishing a student-run free clinic in a major city in Northern Europe: a 1-year experience from Hamburg, Germany
Bibliographic record
Abstract
BACKGROUND: Student-Run Free Clinics (SRFCs) have been an integral part of US medical schools since the 1960s and provide health care to underserved populations. In 2018, we established an SRFC in Hamburg, Germany, a major city in Northern Europe. The aim of this study was to describe the central problems and to investigate the usefulness of an SRFC in a country with free access to medical care, such as Germany. METHODS: All consecutive patients treated at the SRFC Hamburg between February 2018 and March 2019 that consented to this study were analyzed regarding clinical characteristics, diagnosis, readmission rate and country of origin. RESULTS: Between February 2018 and March 2019, 229 patients were treated at the SRFC in Hamburg. The patients came from 33 different countries with a majority (n = 206, 90%) from countries inside the European Union. The most common reasons for visiting the SRFC were infections (23.2%), acute or chronic wounds (13.5%) and fractures (6.3%). CONCLUSION: Our multicultural patients suffer mainly from infections and traumatological and dermatological diseases. We find similarities to published Canadian SRFC patient cohorts but differences in diseases and treatment modalities compared to US SRFCs. Importantly, we demonstrate the relevance and necessity of the SRFC in a major city in Northern Europe.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".