Qualitative Research in Communities of Color: Challenges, Strategies, and Lessons
Bibliographic record
Abstract
In its quest to improve social work practice and design interventions to reach a broader swath of its diverse clientele, the discipline of social work continues to recognize and encourage research within underserved communities of color. Researchers are increasingly turning to qualitative methods to better understand marginalized groups and the nuances of their experiences. Although there are a number of benefits to conducting qualitative research within communities of color, researchers frequently encounter challenges, which range from access to community members to getting participants to speak on topics that they and/or their communities consider to be private or taboo. Research strategies thus far have been directed at researchers who are narrowly defined as “outsiders” or have recommended including scholars of color as members of the research team. However, barriers can persist. Thus, our group of authors—four researchers of color and one white researcher with experience conducting community-based participatory research within marginalized communities where she had previous relationships—share our experiences in this paper. We highlight lessons learned and effective strategies that scholars, especially scholars of color, may use in qualitative research with marginalized and underserved communities, including communities of color.
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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 | 0.518 | 0.314 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.033 | 0.048 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.014 | 0.036 |
| Research integrity | 0.012 | 0.016 |
| 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".