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.
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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 | 0.568 | 0.507 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.023 | 0.037 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".