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Record W4323652203 · doi:10.1080/07481187.2023.2180693

Newspaper coverage of advance care planning during the COVID-19 pandemic: Content analysis

2023· article· en· W4323652203 on OpenAlexaffabout
Doris van der Smissen, Marleen van Leeuwen, Rebecca L. Sudore, Jonathan Koffman, Daren K. Heyland, Agnes van der Heide, Judith Rietjens, Ida J. Korfage

Bibliographic record

VenueDeath Studies · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsQueen's University
FundersNational Institute on AgingNational Institutes of Health
KeywordsNewspaperSensationalismPandemicCoronavirus disease 2019 (COVID-19)Content analysisAdvance care planningPublic health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePsychologyHistoryPolitical scienceHealth careMedia studiesSociologyLawNursingSocial scienceVirologyPathologyOutbreakDisease

Abstract

fetched live from OpenAlex

COVID-19 may cause sudden serious illness, and relatives having to act on patients’ behalf, emphasizing the relevance of advance care planning (ACP). We explored how ACP was portrayed in newspapers during year one of the pandemic. In ‘LexisNexis Uni’, we identified English-language newspaper articles about ACP and COVID-19, published January–November 2020. We applied content analysis; unitizing, sampling, recording or coding, reducing, inferring, and narrating the data. We identified 131 articles, published in UK (n = 59), Canada (n = 32), US (n = 15), Australia (n = 14), Ireland (n = 6), and one each from Israel, Uganda, India, New-Zealand, and France. Forty articles (31%) included definitions of ACP. Most mentioned exploring (93%), discussing (71%), and recording (72%) treatment preferences; 28% described exploration of values/goals, 66% encouraged engaging in ACP. No false or sensationalist information about ACP was provided. ACP was often not fully described. Public campaigns about ACP might improve the full picture of ACP to the public.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.124
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.438
GPT teacher head0.505
Teacher spread0.067 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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