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Record W2558119469

Discourse / Discours - Metaphors and Medication: Understanding Medication Use by Seniors in Everyday Life

2012· article· en· W2558119469 on OpenAlexvenueno aff
Rosanne Beuthin, Ann Holroyd, Peter H. Stephenson, Britt Vegsund

Bibliographic record

VenueCanadian Journal of Nursing Research · 2012
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeMetaphorGrounded theoryQualitative researchObject (grammar)Everyday lifePsychologyLiteral and figurative languageDiscourse analysisNarrative inquirySociologyLinguisticsEpistemologySocial sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the use of metaphor by independent seniors taking medication for chronic health conditions. Narratives from a larger study using grounded theory were analyzed using constant comparative analysis and induction. A secondary analysis of the narratives of 21 participants was undertaken. Transcripts were read line-by-line and all relevant language was highlighted and reviewed with the aim of identifying relationships and themes. The narratives revealed a diverse range of metaphoric language. Four categories were identified: being shackled, hope, external authority, and communication fears. Three additional themes were interwoven into the narratives: aging and death, medication personified, and the body as object. The authors conclude that metaphor reveals the tension and unresolved dilemmas faced by seniors with regard to medication use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0050.012
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0020.003
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 head0.150
GPT teacher head0.430
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes1
Has abstractyes

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