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Record W3122263975 · doi:10.6000/1929-4409.2020.09.236

Criminological Assessment of Medical Misconduct during the COVID-19 Pandemic

2022· article· en· W3122263975 on OpenAlexvenueno aff
Mykola Inshyn, Oleg Sainetsky, Volodymyr Melnyk, Olena A. Hubska, Khrystyna Dzhura

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicContext (archaeology)MisconductCriminologyPolitical sciencePoliticsCoronavirus disease 2019 (COVID-19)LawEmpirical researchPublic relationsSociologyMedicine

Abstract

fetched live from OpenAlex

This study examines the professional actions of health workers during the spread of the COVID-19 pandemic from the point of view of criminological assessment in the context of labor relations. With the help of political and legal analysis and a comparative legal method, the work considers the international practice of assessing the actions of medical workers from the standpoint of criminal responsibility, political and legal initiatives concerning the protection of the rights of medical workers during the spread of a pandemic and quarantine measures. This study argues for significantly broader criminal immunity for health workers during a pandemic. At the same time, the study gives grounds to assert that today the lack of sufficient empirical data and research complicates an objective assessment of the offender. At the same time, based on the results of this study, it is suggested that in the near future the number of investigations and appeals to the court on issues related to the professional activities of medical workers carried out during the spread of the pandemic will increase..

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.362
GPT teacher head0.560
Teacher spread0.198 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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