Mental health and ‘The Big Society’: Where do counselling psychologists and therapists fit in?
Bibliographic record
Abstract
Content and Focus In this paper, we use ‘depression’ as a vehicle for suggesting that current approaches to diagnosing and managing ‘common mental health disorders’ are increasingly untenable in a time of cuts to health care expenditure and changing perspectives on mental health and well-being. We suggest that a collective, community-based response to the human need and suffering encapsulated by such an apparently common condition is possible. However, this would require the individual psychologist or therapist to be much more embedded in the communities they serve. Conclusions We recognise that such an approach has practical and political challenges and is not a cheap option but nonetheless have potential to improve outcomes for clients and communities.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".