An Application of a Community Psychology Approach to Dealing with Farm Stress
Bibliographic record
Abstract
Community psychology arose in the 1960s, in the United States, as a response to: (a) pressure to move toward more community based mental health services, and (b) clinical psychologists asking themselves why they were individually treating and conceptualizing large numbers of people who had similar presenting problems. They realized that the social context of their clients' lives was paramount in determining their emotional health. In 1990, I brought a community psychology perspective to designing and implementing a farm stress program in Saskatchewan. I describe how community psychology values and practices, community development, and mental health promotion are applied to this program. I discuss the value of conceptualizing mental health issues, such as farm stress, from the perspective of individuals-in-communities and discuss considerations for future applications of a community psychology approach to similar and other mental health issues.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".