Experiences in Broker-Facilitated Participatory Cross-Cultural Research
Bibliographic record
Abstract
Health researchers are increasingly using community-based participatory research approaches because of the benefits accrued through ongoing community engagement. The documentation of our research partnership highlights key ethical and analytical challenges researchers face in participatory research, particularly in projects partnering with service providers or cultural brokers in cross-cultural settings. In this article, we describe how choices made to accommodate a participatory research approach in the examination of vaccination behavior impacted the process and outcomes of our qualitative inquiries. First, we found that employing multiple interviewers influenced the breadth of discussion topics, thus reducing the ability to achieve saturation in small study populations. This was mitigated by (a) having two people at each interview and (b) using convergent interviewing, a technique in which multiple interviewers discuss and include concepts raised in interviews in subsequent interviews to test the validity of interview topics. Second, participants were less engaged during the informed consent process if they knew the interviewer before the interview commenced. Finally, exposing identity traits, such as age or immigration status, before the interview affected knowledge cocreation, as the focus of the conversation then mirrored those traits. For future research, we provide recommendations to reduce ethical and analytical concerns that arise with qualitative interview methods in participatory research. Specifically, we provide guidance to ensure ethical informed consent processes and rigorous interview techniques.
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
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 | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Empirical 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 | 0.077 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".