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Record W4390789467 · doi:10.4081/qrmh.2023.11336

The use of metaphors by service users with diverse long-term conditions: a secondary qualitative data analysis

2023· article· en· W4390789467 on OpenAlexaboutno aff
Heidi Lempp, Chris Tang, Emily Heavey, Katherine Bristowe, Helen Allan, Vanessa Lawrence, Beatriz Santana Suárez, Ruth Williams, Lisa Hinton, Karen Gillett, Anne Arber

Bibliographic record

VenueQualitative Research in Medicine & Healthcare · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersMenzies Centre for Australian Studies, King's College London, University of LondonKing's College London
KeywordsMetaphorQualitative researchAffect (linguistics)PsychologyQualitative propertyQuarter (Canadian coin)Health professionalsQualitative analysisFocus groupPopulationMedicineService (business)Term (time)Health careNursingSociologyComputer scienceCommunicationBusiness

Abstract

fetched live from OpenAlex

Long-term conditions and accompanied co-morbidities now affect about a quarter of the UK population. Enabling patients and caregivers to communicate their experience of illness in their own words is vital to developing a shared understanding of the condition and its impact on patients' and caregivers' lives and in delivering person-centred care. Studies of patient language show how metaphors provide insight into the physical and emotional world of the patient, but such studies are often limited by their focus on a single illness. The authors of this study undertook a secondary qualitative data analysis of 25 interviews, comparing the metaphors used by patients and parents of patients with five longterm conditions. Analysis shows how similar metaphors can be used in empowering and disempowering ways as patients strive to accept illness in their daily lives and how metaphor use depends on the manifestation, diagnosis, and treatment of individual conditions. The study concludes with implications for how metaphorical expressions can be attended to by healthcare professionals as part of shared care planning.

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)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.010
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.537
GPT teacher head0.612
Teacher spread0.074 · 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 designQualitative
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

Citations8
Published2023
Admission routes1
Has abstractyes

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