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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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 | 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 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 | 0.191 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".