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Record W4200178352 · doi:10.1163/17087384-12340090

Exploring the Basis for the Increasing Medical Negligence Claims in South Africa

2021· article· en· W4200178352 on OpenAlexvenueno aff
Rendani Margaret Matumba, Anthony O. Nwafor, Edward V. Lubisi, Koboro J Selala

Bibliographic record

VenueAfrican Journal of Legal Studies · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesJurisprudenceWitnessGovernment (linguistics)Health careBusinessMedical negligenceLawExpert witnessService (business)Healthcare servicePolitical scienceLaw and economicsEconomics

Abstract

fetched live from OpenAlex

Abstract Litigation arising from medical negligence have continued to witness an incremental trajectory in the contemporary South African medical jurisprudence. As the number of claims continue to rise, so also does the financial expense in the form of cost of litigation on the part of the litigants and damages paid by the healthcare personnel and government agencies in successful cases. Such expense, however, palls into oblivion when compared with the reputational damage attendant such negligent conducts on the parts of both the healthcare personnel and the healthcare institutions. On the positive side, however, is that the growing instances of such claims have brought to the fore the need to interrogate the reasons and seek solutions with a view to attaining a more efficient health service delivery system in the country.

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)0.018
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 score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.004
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.325
GPT teacher head0.455
Teacher spread0.130 · 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
Published2021
Admission routes1
Has abstractyes

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