A Participatory Group Process to Analyze Qualitative Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: When conducting community-based participatory research (CBPR), community researchers are often consulted during the analysis step, but rarely participate in the entire process. OBJECTIVES: This paper describes a participatory qualitative data analysis process that was used in three projects with marginalized women in Ontario, Canada. In each project, marginalized women were trained as Inclusion Researchers (IRs) and participated in all stages of the research process. Given the emphasis of the projects on inclusion, it was important that a data analysis process be developed that was group oriented, engaging, understandable, and inclusive of the community researchers. METHODS: A five-part analysis process is described including preparation of the data, grouping and coding, consolidation, making sense of the data, and producing a report. This group analysis process took place over 2 full days with facilitation by an academic researcher, Details about the techniques used for each step are described. CONCLUSIONS: The strengths of this participatory qualitative data analysis process were that it enabled participation of people with a mixture of levels of education and familiarity with analysis; it enabled community member control of the interpretation; and it could handle large volumes of data quickly. The main limitation was that additional time and procedures would be necessary for a deeper analysis or for groups of over 25 participants. The factors that contributed to the success of this participatory analysis process included accessible and clear procedures, use of visual grouping techniques, and a positive and supportive atmosphere for participation.
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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 | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it