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Record W2105407940 · doi:10.1177/160940690800700102

Qualitative Interviewing Using Interpersonal Process Recall: Investigating Internal Experiences during Professional-Client Conversations

2008· article· en· W2105407940 on OpenAlexaff
Denise J. Larsen, Keri Flesaker, Rachel Stege

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2008
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInterviewRecallNegotiationFraming (construction)Interpersonal communicationPsychologyQualitative researchProcess (computing)Medical educationApplied psychologySocial psychologySociologyComputer scienceMedicineEngineering

Abstract

fetched live from OpenAlex

Interpersonal process recall (IPR) interviewing uses video-assisted recall to access conscious yet unspoken experiences in professional caregiving interactions. Although IPR has been employed across the helping professions, little has been written about effectively conducting IPR interviews. Drawing on their IPR-based counseling research on hope, the authors provide a framework for the use of IPR interview strategies and for addressing challenges unique to IPR. Specific issues include (a) preparing the research team and setting, (b) issues specific to IPR interviewing, including framing IPR questions, (c) working with heightened emotion, and (d) negotiating professional/researcher roles. Finally, they discuss participant experiences and potential applications of IPR.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.592
GPT teacher head0.648
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 teacher head, not a consensus.

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

Citations130
Published2008
Admission routes1
Has abstractyes

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