Training to be a Community Psychologist in the Age of a Digital Revolution
Bibliographic record
Abstract
Reflecting on pedagogy and curricula that have shaped the field of community psychology, we review the history of training community psychologists since the field’s inception in the United States. We then examine relevant academic literature documenting how digital technologies in the 21st century have been successfully used in community-based participatory research (CBPR) studies conducted by community psychologists to promote engaged scholarship, the field’s core values (e.g. sense of community, social justice, collaboration), and its commitment to social change. While early ideas for improving scholars’ training emphasized adopting practices to meet changing community needs, our review of literature on CBPR and other community-engaged scholarly work by community psychologists in the last two decades has revealed that digital technologies’ ability to promote the field’s values and goals still needs to be fully harnessed. Lastly, we offer practical recommendations for community psychology undergraduate and graduate training programs to consider and implement so they can incorporate digital technologies into their programs and harness their potential to promote engaged scholarship, the field’s core values, and its commitment to social change.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".