“This Is a Look Into My Life”: Enhancing Qualitative Inquiry Into Communication Through Arts-Based Research Methods
Bibliographic record
Abstract
As researchers in communication sciences and related disciplines continue to embrace qualitative research methodologies, there will be a corresponding need for innovative methods and strategies for data collection and generation, reflexivity and knowledge translation to make qualitative research methods more inclusive. Historically, those who communicate differently from the status quo have been excluded from or spoken for in qualitative research. The resulting omission of these perspectives in the literature constitutes a critical research-to-practice gap as clinicians seek to deliver client-focused communication care. Arts-Based Research (ABR) offers researchers a means of augmenting potentially linguistically and cognitively demanding verbal interviews while inviting participants to share a window into their daily lives with researchers and knowledge users. Additionally, ABR offers participants alternative forms of expression and creates avenues for researcher reflexivity and participant-researcher reflexive dialogue. To illustrate how ABR can enhance communication research, we present reflections on a study that incorporated multi-media artistic materials in a reflexive arts-based collective case study design. Working with young people experiencing post-concussion communication changes, we used arts-based materials collected and created by participants to support and enhance data collection. These materials were analyzed as part of each case, across cases, and played a central role in sharing our findings. Additionally, arts-based materials facilitated participant-researcher reflexive dialogue and researcher reflexivity. The goals of this paper are to: 1) provide a brief overview of ABR as a method for researchers interested in communication; 2) discuss four ways to incorporate ABR in the research process; 3) provide a case example illustrating different types of creative works to illustrate the four ways ABR can enhance communication research; and 4) discuss benefits, considerations and implications of using ABR to support inclusive research design.
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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 | 0.223 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".