Implications of Community-Based Research for Professional Psychology Training: Reflections from Two Early Career Psychologists
Bibliographic record
Abstract
Community psychology (CP) has valuable philosophical perspectives and methodological approaches to offer the wider discipline of psychology, yet it remains underappreciated and often invisible in most professional training programs in psychology, including those programs intended to train in the areas of clinical, counselling, school, and neuropsychology. Community-based research (CBR) is one particular methodological approach within CP that has the potential to enhance standard research training experiences, as well as to enhance professional psychology training more generally. In this paper, we discuss the professional psychology training implications of CBR approaches, highlighting potential changes to the existing training structure that could facilitate wider access to training in CBR, and thereby enhance the competencies of professional psychologists. We also critically reflect on our experiences conducting our own CBR dissertation projects while becoming trained as clinical psychologists. We encourage other trainees, professional psychologists, and training programs to consider the merits of incorporating CP perspectives and approaches into their work.
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 | 0.083 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.055 | 0.049 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.016 | 0.040 |
| 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".