The Challenges of Including Patients With Aphasia in Qualitative Research for Health Service Redesign: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Aphasia is an impairment of language, affecting the production or comprehension of speech and the ability to read or write. Aphasia is a frequent complication of stroke and is a major disability for patients and their families. The provision of services for stroke patients differs across health care providers and regions, and strategies directed at improving these services have benefited from the involvement of patients. However, patients with aphasia are often excluded from these co-design activities due to a diminished capacity to communicate verbally and a lack of health researcher experience in working with patients with aphasia. OBJECTIVE: The primary aim of this paper is to identify approaches appropriate for working with patients with aphasia in an interview situation and, more generally, determine the importance of including people with aphasia in health service improvement research. The secondary aim is to describe the experiences of researchers involved in interviewing patients with aphasia. METHODS: A total of 5 poststroke patients with aphasia participated in face-to-face interviews in their homes to gain insight into their in-hospital experience following their stroke. Interviews were audio-recorded, and thematic analysis was performed. The experiences of the researchers interviewing these patients were informally recorded postinterview, and themes were derived from these reflections. RESULTS: The interview technique utilized in this study was unsuitable to gain rich, qualitative data from patients with aphasia. The experience of researchers performing these interviews suggests that preparation, emotion, and understanding were three of the main factors influencing their ability to gather useful experiential information from patients with aphasia. Patients with aphasia are valuable contributors to qualitative health services research, and researchers need to be flexible and adaptable in their methods of engagement. CONCLUSIONS: Including patients with aphasia in health service redesign research requires the use of nontraditional interview techniques. Researchers intending to engage patients with aphasia must devise appropriate strategies and methods to maximize the contributions and valuable communications of these participants.
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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 | 0.103 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".