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Record W2149009212 · doi:10.1177/107484070000600404

Conjoint Research Interviews With Frail, Elderly Couples: Methodological Implications

2000· article· en· W2149009212 on OpenAlexaff
Frances E. Racher, Joseph M. Kaufert, Betty Havens

Bibliographic record

VenueJournal of Family Nursing · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of ManitobaBrandon University
Fundersnot available
KeywordsDyadConversationPsychologyConstruct (python library)Unit (ring theory)Data collectionSocial psychologyConversation analysisQualitative researchContent analysisNonverbal communicationApplied psychologyDevelopmental psychologySociologyComputer scienceCommunicationMathematics education

Abstract

fetched live from OpenAlex

In this phenomenological study, frail, rural elderly couples were interviewed as dyads. Couples participated in semistructured interviews and jointly constructed their responses. The elderly couple or dyad was the unit of inquiry, data collection, and analysis. The study sought to maximize the understanding of the couple as a unit as partners negotiated and constructed their responses. The method of data collection provided opportunity to observe the verbal and nonverbal interaction of the couple, the process used to construct the conjoint dialogue, and the content of the discourse. The couple conversation was richer in content and more effective in addressing the research question than were individual interviews. This article focuses on the methodological issues as they relate to the couple as the dyadic unit of research. Dialogue from the study illustrates the richness of the data gathered using this method.

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)0.160
metaresearch head score (Gemma)0.158
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: Methods · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.158
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0170.012
Scholarly communication0.0080.008
Open science0.0040.011
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.520
GPT teacher head0.575
Teacher spread0.056 · 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
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

Citations23
Published2000
Admission routes1
Has abstractyes

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