A scoping review: The utility of participatory research approaches in psychology
Bibliographic record
Abstract
Consistent with community psychology's focus on addressing societal problems by accurately and comprehensively capturing individuals' relationships in broader contexts, participatory research approaches aim to incorporate individuals' voices and knowledge into understanding, and responding to challenges and opportunities facing them and their communities. Although investigators in psychology have engaged in participatory research, overall, these approaches have been underutilized. The purpose of this review was to examine areas of research focus that have included participatory research methods and, in turn, highlight the strengths and ways that such methods could be better used by researchers. Nearly 750 articles about research with Indigenous Peoples, children/adolescents, forensic populations, people with HIV/AIDS, older adults, and in the area of industrial-organizational psychology were coded for their use of participatory research principles across all research stages (i.e., research design, participant recruitment and data collection, analysis and interpretation of results, and dissemination). Although we found few examples of studies that were fully committed to participatory approaches to research, and notable challenges with applying and reporting on this type of work, many investigators have developed creative ways to engage respectfully and reciprocally with participants. Based on our findings, recommendations and suggestions for researchers are discussed.
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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 | 0.369 | 0.539 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.043 | 0.038 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".