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Record W4403850967 · doi:10.5539/ijel.v14n6p57

Get Peace of Mind with Do-It-Yourself Living Wills: The Digitalisation of Medical-Legal Practice and the New Post-Covid 19 Approach

2024· article· en· W4403850967 on OpenAlexvenueno aff
Michela Giordano

Bibliographic record

VenueInternational Journal of English Linguistics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)LawSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medical practicePolitical scienceBusinessLaw and economicsSociologyMedicineMedical educationPathology

Abstract

fetched live from OpenAlex

“The automatisation of the core tasks performed by legal practitioners […] is an ongoing and open-ended process” (Schäfke-Zell & Asmussen, 2019, p. 65). “A host of innovative legal-tech companies have entered the market of legal service providers, presently challenging the lawyers’ monopoly over the practice of the law, and ultimately, altering the mode of production in the legal field” (Caserta, 2020, p. 1). Beginning with these assumptions, this study draws on ongoing research on healthcare powers of attorney, living wills, and advance directives (Giordano, 2019, 2021) and will explore the current radical changes and restructuring of modalities of production in medical-legal documents and in the delivery of legal services. The legal websites in the corpus under investigation provide guidance in creating a personal living will according to well-established legal procedures that are pursuant to the laws and regulations of a particular state or country. Living wills and advance directives are signed documents in which the declarants state whether or not they wish to be kept alive using artificial means in the event a doctor declares their death to be imminent. Doctors and medical practitioners are legally bound to follow these directives. Following the increased digitalization of the legal field, these documents are now frequently drafted and completed online, thus embracing the full potential of the new technologies available today. Both verbal and visual rhetoric (Murray, 2014; Sherwin, Feigenson, & Spiesel, 2005) will be investigated to ascertain whether and to what extent digital transformation is redefining the ways in which this legal service is offered, thus promoting greater understanding and wider knowledge of a controversial issue such as making decisions regarding one’s end of life and the choices available among various life-prolonging measures and treatments. The research aims to determine whether the new digital genres can help translate the specific constraints and demands of legal argumentation into new popular, cultural and technological discourse which better appeals to peoples’ emotions and values. The legal sector’s increasing use of digital forms and templates embellished with visual and multimedia content may at once have a commodifying and democratizing effect. Legal meanings and services in the new legal market (Schäfke-Zell & Asmussen, 2019, p. 66) might be better understood and internalized, thus reinforcing and disseminating the emerging vernacular of digital culture along with law, regulations and legal institutes and instruments, which would otherwise be difficult for a layperson to comprehend or accept (Anesa, 2016; Calsamiglia, 2003; Calsamiglia & van Dijk, 2004). In addition to this process of democratization of new media, the Coronavirus pandemic also contributed to changing people’s approach to life (Garzone, 2023) and to living wills, triggered by an increased sense of precariousness of life and imminence of death. English-speaking countries, in particular, have seen a surge in people updating and altering advance directives as a result of the complications caused by the disease; different steps might be taken now, when current documents may be dangerously inadequate. The best ways to update existing texts to the new post-Covid 19 conditions therefore call for thorough investigation.

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)0.007
metaresearch head score (Gemma)0.593
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.593
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.413
Teacher spread0.379 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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