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Record W2996544649 · doi:10.1093/pubmed/fdz165

Establishing a student-run free clinic in a major city in Northern Europe: a 1-year experience from Hamburg, Germany

2019· article· en· W2996544649 on OpenAlexaboutno aff
Richard Drexler, Felix Fröschle, Christopher Predel, Berit Sturm, Klara Ustorf, Louisa Lehner, Jara Janzen, Lisa Valentin, Tristan Scheer, Franziska Lehnert, Refmir Tadžić, Karl J. Oldhafer, Tobias Meyer

Bibliographic record

VenueJournal of Public Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePublic healthEuropean unionFamily medicineHealth careEpidemiologyDemographyPediatricsNursingEconomic growthInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.094
GPT teacher head0.430
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2019
Admission routes1
Has abstractyes

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