Quantitative Methods in Participatory Research: Being Sensitive to Issues of Scientific Validity, Community Protection, and the Academic-Community Relationship
Bibliographic record
Abstract
Background: From conducting a large systematic realist review of the Community-Based Participatory Research literature, we conceptualize participatory research (PR) as an approach to research through which stakeholders can implement quantitative, qualitative or mixed methods study designs. We will highlight the issues involved in co-constructing quantitative research such as randomized control trials in a community-based setting. Method and Results: The review team included experts in CBPR and realist review and was a collaboration between researchers and decision-making partners in public health, research funding, ethics review and community-engaged scholarship. 7167 abstracts were identified, 591 full-text papers selected and 83 sets of publications appraised. Of those, 23 CBPR health interventions were synthesized (comprising 276 publications). All twenty three partnerships from our review employed quantitative methods in their research designs involving observational and/or experimental designs and mixed methods research. To illustrate how partnerships used such methods and the issues which emerged, we draw on three partnerships as case studies. The examples presented here will demonstrate that commitment to dialogue and consensus building among partnership stakeholders is key to ensuring that methods do not generate scientifically valid research at the cost of community harm, loss of reputation, stigma or generating feelings of denial, betrayal and exclusion. Conclusion: PR can enhance the implementation of quantitative methods by ensuring that community interests are met at every step in the process. The partnered approach also provides the opportunity for community members to learn and recognize the benefits of conducting scientific research, while at the same time protecting community interests and the academic-community relationship. The outcome of ongoing dialogue between stakeholders on quantitative research methods can vary; however, coming to consensus, regardless of the outcome, through dedicated communication, patience, understanding, humility, and flexibility strengthens the trust bond in these partnerships, which facilitates future project and infrastructure developments.
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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 | 0.654 | 0.658 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".