Participatory Member Checking: A Novel Approach for Engaging Participants in Co-Creating Qualitative Findings
Bibliographic record
Abstract
Member checking is a technique which aims to increase the trustworthiness or rigour of qualitative research by asking participants to comment on study findings. However, traditional methods of member checking (e.g., transcript reviews) face scrutiny for being ineffective or tokenistic ways of eliciting participant feedback. Emerging member checking approaches seek to evoke feedback in more meaningful ways. While these alternatives have merit, persistent challenges include eliciting critical feedback, time constraints, supporting an ongoing dialogue with participants, and setting future research directions. To address these challenges, we introduce a novel alternative to member checking, “Participatory Member Checking” (PMC). PMC draws from the principles of Patient Engagement (a participatory approach) and promotes the co-creation of qualitative research findings between participants and researchers across five steps: (1) Elicit Feedback, (2) Summarize Feedback, (3) Check for Understanding, (4) Implement Feedback, and (5) Demonstrate Accountability. PMC encourages critical feedback, is practical and efficient, promotes ongoing dialogue through both written and verbal feedback, and involves participants in setting future research directions. The present article presents PMC in the context of a qualitative study exploring patient partners’ experiences of being engaged in research projects supported by the Canadian Institutes of Health Research. We describe PMC in sufficient detail to facilitate uptake by other researchers, show how PMC meaningfully impacted our research findings, and demonstrate the acceptability of PMC among a group of 11 participants (Median age = 62, range = 25–82, 81.8% women). Considerations for adopting PMC in future research are discussed.
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How this classification was reachedexpand
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 | 0.042 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".