Early palliative care perceptions by patients with cancer and primary caregivers: metaphorical language
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".