MétaCan
Menu
Back to cohort
Record W2537264758 · doi:10.1556/650.2016.30510

Nyugdíjas orvosok helyzete Magyarországon – országos, reprezentatív felmérés eredményei alapján

2016· article· en· W2537264758 on OpenAlexaboutno aff
Zsuzsa Győrffy, Zsuzsanna Szél, Edmond Girasek

Bibliographic record

VenueOrvosi Hetilap · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)DemographyGerontologyEpidemiologyMedicineQuality of life (healthcare)PopulationPopulation ageingPsychologyGeographySociology

Abstract

fetched live from OpenAlex

INTRODUCTION: The aging population and the aging physician society is an important challenge of the New Millenium. Despite this, very few publications are dealing with the older generations' physical and mental well-being, quality of life and working conditions. AIM: The aim of this study was to describe the retired physicians populations' (n = 2112) demographic data, work status, income and health status. METHOD: Data of this representative, cross-sectional epidemiological study was obtained from online and paper-based questionnaires completed by 2112 retired physicians. RESULTS: The retired physicians' average age is 72 years, nearly two-thirds of the respondents retired after 35-45 years of service. Currently, nearly 60% are working, almost a quarter of them more than 40 hours per week. 35% of the respondents' income is below HUF 150,000. On this issue, significant differences emerge between female doctors and their male colleagues. CONCLUSIONS: The employment data of the results is consistent with the international trend, but the gender perspectives has unique significance in the international literature. Orv. Hetil., 2016, 157(43), 1729-1736.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.198
GPT teacher head0.428
Teacher spread0.230 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueOrvosi HetilapSame topicRetirement, Disability, and EmploymentFrench-language works237,207