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Record W2016224775 · doi:10.1177/135910530000500305

Levels of Narrative Analysis in Health Psychology

2000· article· en· W2016224775 on OpenAlexaff
Michael Murray

Bibliographic record

VenueJournal of Health Psychology · 2000
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNarrativeNarrative inquiryNarrative networkNarrative criticismInterpersonal communicationContext (archaeology)PsychologySocial psychologyNarrative psychologyNarrative historyHealth psychologyMeaning (existential)SociologyPublic healthLiteratureHistoryMedicinePsychotherapistArt

Abstract

fetched live from OpenAlex

The past 10-15 years have seen a rapid increase in the study of narrative across all the social sciences. It is sometimes assumed that narrative has the same meaning irrespective of the context in which it is expressed. This article considers different levels of narrative analysis within health psychology. Specifically, it considers the character of health and illness narratives as a function of the personal, interpersonal, positional and societal levels of analysis. At the personal level of analysis narratives are portrayed as expressions of the lived experience of the narrator. At the interpersonal level of analysis the narrative is one that is co-created in dialogue. At the positional level of analysis the analysis considers the differences in social position between the narrator and the listener. The societal level of analysis is concerned with the socially shared stories that are characteristic of certain communities or societies. The challenge is to articulate the connections between these different levels of narrative analysis and to develop strategies to promote emancipatory narratives.

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)0.013
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0090.042
Scholarly communication0.0220.019
Open science0.0020.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.070
GPT teacher head0.463
Teacher spread0.393 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations243
Published2000
Admission routes1
Has abstractyes

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