MétaCan
Menu
Back to cohort
Record W7135451173

Opinions and Attitudes of Patients to Differences in Health Care Systems in the Czech Republic and Germany

2015· dissertation· cs· W7135451173 on OpenAlexaboutno aff
Klára Pousková

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2015
Typedissertation
Languagecs
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsCzechHealth careGermanPharmacyPublic healthWest germanyQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Charles University in Prague, Faculty of Pharmacy in Hradci Králové Department of Social and Clinical Pharmacy Student: Klára Pousková Supervisor: PharmDr. Jan Kostřiba, Ph.D. Title of thesis: Opinions and Attitudes of Patients to Differences in Health Care Systems in the Czech Republic and Germany Key words: health care systems, extra care, questionnaire survey, Czech Republic, Germany Introduction: Czech Republic and Germany are two neighboring countries, located in central Europe, but there are number of differences between them. This thesis focuses on the differences in their health systems. Official statistics provide an objective view on the situation in both healthcare systems. Nevertheless those statistics don't include patient's opinions. A questionnaire survey was therefore created to include their point of view. Objectives: The aim of this thesis was to compare selected characteristics of the Czech and German health care systems, to recognize the major differences between them and to identify their greatest advantages and disadvantages. Methods: The survey was realized in selected towns in the Czech Republic and in Germany. All pharmacies in these towns were asked to take part. Patients were interviewed with adherence to the created questionnaire. The interview was conducted by the...

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.313
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueDigital Repository (National Repository of Grey Literature)Same topicMedication Adherence and ComplianceFrench-language works237,207