Analysis of education of pharmacists in the Czech Republic after graduation.
Bibliographic record
Abstract
1 Abstract Analysis of education of pharmacists in the Czech Republic after graduation Author: Eliška Röslerová Tutor: PharmDr. Josef Malý, Ph.D. Consultant: Mgr. Aleš Krebs, Ph.D. Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Králové, Charles University Introduction and aim of study: A Pharmacist, as a worker in a health service has to continue to further the education of themselves throughout their career in pharmacy. The main goal of this diploma thesis was to analyse continuing education after graduation used by selected members of the District Association of Pharmacists. Methods: Data for a practical part of this diploma thesis were collected by a questionnaire survey. A 39-item survey was administered to selected pharmacists (members in the Czech Chamber of Pharmacists) from randomly chosen the District Associations of Pharmacists. The address to access the survey was added in a recruitment letter. In this letter, the study was also described and was sent to 858 pharmacists through their e-mail addresses. The first mailing was sent after 7 days, the second mailing after 14 days. The respondents who filled in compulsory questions were included in the analysis. Descriptive statistics were used for analysing the results of the survey. Results: The survey of the 299 respondents...
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".