Facilitating Interviews in Qualitative Research With Visual Tools: A Typology
Bibliographic record
Abstract
Visual methods are gaining traction in qualitative research to support data generation, data analysis, and research dissemination. In this article, I propose a preliminary typology that categorizes five identified purposes of applying visual methods in qualitative interviews: to (a) enable communication, (b) represent the data, (c) enhance data quality and validity, (d) facilitate the relationship, and (e) effect change. Examples of visual tools are presented to demonstrate their utility in addressing these five aims. An existing ethical framework for visual tool use in qualitative research is then presented to structure a discussion on ethical considerations related to confidentiality, consent, representations and audiences, fuzzy boundaries between researchers and participants, authorship and ownership, and minimizing harm. Future directions include testing and extending the typology with respect to other visual methods and qualitative research processes, and research to evaluate the effectiveness of various visual tools at achieving the aims represented in the typology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.141 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".