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Record W4391449817 · doi:10.2196/52096

The Development and Use of a New Visual Tool (REVISIT) to Support Participant Recall: Web-Based Interview Study Among Older Adults

2024· article· en· W4391449817 on OpenAlex
Eileen Dryden, Chitra Anwar, Jennifer Conti, Jacqueline Boudreau, Meaghan A. Kennedy, William W. Hung, Kathryn Nearing, Camilla B. Pimentel, Lauren R. Moo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsnot available
FundersOffice of Rural HealthRehabilitation Research and Development ServiceHealth Services Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsInterviewRecallFocus groupQualitative researchPsychologyMedical educationApplied psychologyParticipant observationHealth careTelemedicineNursingMedicineCognitive psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Qualitative health services research often relies on semistructured or in-depth interviews to develop a deeper understanding of patient experiences, motivations, and perspectives. The quality of data gathered is contingent upon a patient's recall capacity; yet, studies have shown that recall of medical information is low. Threats to generating rich and detailed interview data may be more prevalent when interviewing older adults. OBJECTIVE: We developed and studied the feasibility of using a tool, Remembering Healthcare Encounters Visually and Interactively (REVISIT), which has been created to aid the recall of a specific telemedicine encounter to provide health services research teams with a visual tool, to improve qualitative interviews with older adults. METHODS: The REVISIT visual appointment summary was developed to facilitate web-based interviews with our participants as part of an evaluation of a geriatric telemedicine program. Our primary aims were to aid participant recall, maintain focus on the index visit, and establish a shared understanding of the visit between participants and interviewers. The authors' experiences and observations developing REVISIT and using it during videoconference interviews (N=16) were systematically documented and synthesized. We discuss these experiences with REVISIT and suggest considerations for broader implementation and future research to expand upon this preliminary work. RESULTS: REVISIT enhanced the interview process by providing a focus and catalyst for discussion and supporting rapport-building with participants. REVISIT appeared to support older patients' and caregivers' recollection of a clinical visit, helping them to share additional details about their experience. REVISIT was difficult to read for some participants, however, and could not be used for phone interviews. CONCLUSIONS: REVISIT is a promising tool to enhance the quality of data collected during interviews with older, rural adults and caregivers about a health care encounter. This novel tool may aid recall of health care experiences for those groups for whom it may be more challenging to collect accurate, rich qualitative data (eg, those with cognitive impairment or complex medical care), allowing health services research to include more diverse patient experiences.

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.

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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.333
GPT teacher head0.557
Teacher spread0.224 · 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