Qualitative data collection: considerations for people with Aphasia
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.
Bibliographic record
Abstract
Background: Investigators are increasingly using qualitative research methods in studies with people with aphasia. While most qualitative research has relied on the pragmatic method of inquiry, and methods reliant on verbal communication such as interviews, there exists a gap in the literature on how to use these methods with people with communication impairments such as aphasia.Aims: This paper aims to be a starting point for researchers new to qualitative research wanting to learn about how to collect qualitative data from people with aphasia. A secondary aim is to encourage researchers to report the creative ways in which they manage the communication challenges presented by people with aphasia in data collection.Main Contribution: This tutorial provides an overview of qualitative data collection methods and adjustments for making them aphasia-friendly, including interview and alternative interviewing methods, focus groups, observation, and photovoice. Each data collection method is discussed in the context of ethical and logistical considerations specific to people with aphasia.Conclusions: Qualitative data collection with people with aphasia can be challenging due to their communication difficulties, but when done properly researchers can help people with aphasia get their stories and perspectives into the world.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it