Community-Based Research: Perspectives of Psychology Researchers and Community Partners
Bibliographic record
Abstract
There is now a growing understanding that translational research must be co-created in collaboration with community partners and that solutions to real-world social problems require stepping outside the academic silo. Fewer than half of psychology programs in Canada, however, offer courses in community-based research or evaluation, leaving a gap in skill development amongst the next generation of scholars. In an effort to partially fill this learning gap, the current paper provides insights into lessons learned from the perspectives of researchers and community partners alike, who have been mutually engaging in community-based research over the last 25 years. Ultimately this paper seeks to provide a roadmap for conducting community-based research and illustrates why it should be a central component to research seeking to answer critical questions in psychological science. First, we provide a conceptual foundation of community-based research. Next, using three specific community-based research projects as examples, we share the challenges and benefits of conducting research in the community context. Finally, we highlight future directions for increasing the uptake of community-based research in Canada.
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
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 | 0.101 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.052 | 0.141 |
| Scholarly communication | 0.045 | 0.027 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".