Community Psychology and Community Mental Health: A Call for Reengagement
Bibliographic record
Abstract
Community psychology is rooted in community mental health research and practice and has made important contributions to this field. Yet, in the decades since its inception, community psychology has reduced its focus on promoting mental health, well-being, and liberation of individuals with serious mental illnesses. This special issue endeavors to highlight current efforts in community mental health from our field and related disciplines and point to future directions for reengagement in this area. The issue includes 12 articles authored by diverse stakeholder groups. Following a review of the state of community mental health scholarship in the field's two primary journals since 1973, the remaining articles center on four thematic areas: (a) the community experience of individuals with serious mental illness; (b) the utility of a participatory and cross-cultural lens in our engagement with community mental health; (c) Housing First implementation, evaluation, and dissemination; and (d) emerging or under-examined topics. In reflection, we conclude with a series of challenges for community psychologists involved in future, transformative, movements in community mental health.
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 | 0.086 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.070 |
| Scholarly communication | 0.042 | 0.067 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.031 | 0.075 |
| Insufficient payload (model declined to judge) | 0.006 | 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".