Qualitative Methods Case Study: Using MAXQDA in Indigenous HIV Journey Mapping Research
Bibliographic record
Abstract
This study presents a case study using MAXQDA, a Computer-Assisted Qualitative Data Analysis Software (CAQDAS), to analyze interview data within the Northern HIV Journey Mapping Project, which explored the experiences of Indigenous people living with HIV in Manitoba, Canada. By adapting patient experience mapping and process mapping methods, the research team traced participant journeys through the HIV Care Cascade, identifying barriers and facilitators to well-being. Within a decolonizing framework informed by Two-Eyed Seeing and Ethical Space, we critically examined the role of CAQDAS in Indigenous health research, highlighting both its utility and its tensions with Indigenous storytelling traditions. Our methodological approach balanced Western analytical tools with Indigenous knowledge systems, ensuring that technology served the research rather than distorting the lived realities of participants. MAXQDA enabled data visualization that made complex, non-linear healthcare journeys more accessible to researchers and policymakers. However, the software’s structuring of qualitative data into discrete codes and categories raised epistemological questions about how Indigenous narratives are “treated” as data within a neoliberal knowledge economy. To mitigate these concerns, we engaged in reflexivity, involved Indigenous Elders and research associates, and emphasized relational accountability in both analysis and dissemination. This case study contributes to the field of qualitative methods by demonstrating how CAQDAS can be employed within decolonizing Indigenous research while acknowledging its limitations. Results suggest that while tools like MAXQDA enhance methodological rigour and knowledge mobilization, researchers must remain critically engaged with their impact on Indigenous ways of knowing. We recommend that future research prioritizes Indigenous-led adaptations of digital analysis tools and emphasize participatory approaches to ensure that qualitative research serves Indigenous communities in culturally responsive ways. Our reflections offer insights for scholars seeking to decolonize qualitative inquiry while maintaining methodological integrity in health research.
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.
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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".