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Record W4387417179 · doi:10.7176/jlpg/136-03

Comparative Legal Analysis of the Medical Negligence Landscape: The Ghanaian and Commonwealth Criminal Jurisprudence

2023· article· en· W4387417179 on OpenAlexaboutno aff
George Mensah, Alfred Addy, Prince Opuni Frimpong

Bibliographic record

VenueJournal of Law Policy and Globalization · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsJurisprudenceHarmCommonwealthCredibilityLawPolitical science

Abstract

fetched live from OpenAlex

This paper on comparative legal analysis of the medical negligence landscape: the Ghanaian and commonwealth criminal jurisprudence provides a comprehensive analysis of medical negligence and assault in various countries. The case of gross medical negligence is a serious issue that requires immediate attention. Through the analysis of various authorities it becomes evident that there are three main areas contributing to this problem: inaccurate diagnosis and delayed treatment, failure to communicate effectively with patients, and lack of proper supervision and training. The impact of gross medical negligence on patients is profound and far-reaching. It not only causes physical harm but also inflicts emotional and psychological trauma on those affected. In Ghanaian criminal jurisprudence, it is crucial to address this issue to restore public trust in the healthcare system and ensure justice for victims. By implementing stringent legal measures, including vicarious liability, we can uphold professional standards, deter future negligence, and provide recourse for those who have suffered as a result of gross medical negligence. One strength of this article is its extensive coverage of different jurisdictions. By comparing Ghana, the US, UK, Canada, and Australia, the authors provide a global perspective on the issue. This allows readers to understand how different legal systems handle cases of medical negligence and assault. Additionally, the inclusion of multiple studies conducted by different researchers adds credibility to the findings. The authors have effectively synthesized these studies to present a cohesive analysis. Overall, this article serves as a valuable resource for anyone interested in understanding how different countries approach cases of gross medical negligence and assault. Keywords: Gross Medical Negligence, General, Medical Negligence, Double Jeopardy, Ghanaian Criminal Jurisprudence, Medical Assault, Vicarious Liability, Criminal Law, Tort, US Jurisprudence, UK Jurisprudence, Canadian Jurisprudence DOI: 10.7176/JLPG/136-03 Publication date: September 30 th 2023

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.495
Teacher spread0.412 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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