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Record W4396898978 · doi:10.1136/spcare-2024-004842

Early palliative care perceptions by patients with cancer and primary caregivers: metaphorical language

2024· article· en· W4396898978 on OpenAlexaff
Elena Bandieri, Sarah Bigi, Melissa Nava, Eleonora Borelli, Carlo Adolfo Porro, Erio Castellucci, Fabio Efficace, Éduardo Bruera, Oreofe O. Odejide, Camilla Zimmermann, Leonardo Potenza, Mario Luppi

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMetaphorPalliative carePerceptionVariety (cybernetics)Safe havenHavenPsychologyMedicineNursingLinguistics

Abstract

fetched live from OpenAlex

OBJECTIVE: This article reports on the results of an analysis of metaphorical language used by patients diagnosed with advanced cancer and their caregivers receiving early palliative care (EPC). METHODS: Data were collected through a pen-and-paper questionnaire on respondents' perceptions of the disease, its treatment and their idea of death, before and after receiving EPC. The data were analysed by identifying all metaphorical uses of language, following the 'metaphor identification procedure' proposed by the Praggjelaz Group. RESULTS: Metaphors were used from a variety of semantic fields. EPC was described using spiritual terms, to indicate that this approach was instrumental in 'restoring life', 'producing hope' and making patients feel 'accompanied'. The most recurrent metaphors were those referring to light and salvation; spatial metaphors were used to describe the treatment and the hospital as a 'safe haven' and 'an oasis of peace'. Patients and caregivers were overall consistent in the aforementioned ways of referring to illness and treatment; caregivers were more likely than patients to use war metaphors, although their use overall was rare. CONCLUSIONS: Our results suggest that EPC is perceived positively by patients and their caregivers and provide insights regarding the manner in which EPC could be presented to patients, caregivers and 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.333
Teacher spread0.315 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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