The Development and Use of a New Visual Tool (REVISIT) to Support Participant Recall: Web-Based Interview Study Among Older Adults
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".