Resolving the paradox of increased mental health expenditure and stable prevalence
Bibliographic record
Abstract
A doubling of Australian expenditure on mental health services over two decades, inflation-adjusted, has reduced prevalence of neither psychological distress nor mental disorders. Low rates of help-seeking, and inadequate and inequitable delivery of effective care may explain this partially, but not fully. Focusing on depressive disorders, drawing initially on ideas from the work of philosopher and socio-cultural critic Ivan Illich, we use evidence-based medicine statistics and simulation modelling approaches to develop testable hypotheses as to how iatrogenic influences on the course of depression may help explain this seeming paradox. Combined psychological treatment and antidepressant medication may be available, and beneficial, for depressed people in socioeconomically advantaged areas. But more Australians with depression live in disadvantaged areas where antidepressant medication provision without formal psychotherapy is more typical; there also are urban/non-urban disparities. Depressed people often engage in self-help strategies consistent with psychological treatments, probably often with some benefit to these people. We propose then, if people are encouraged to rely heavily on antidepressant medication only, and if they consequently reduce spontaneous self-help activity, that the benefits of the antidepressant medication may be more than offset by reductions in beneficial effects as a consequence of reduced self-help activity. While in advantaged areas, more comprehensive service delivery may result in observed prevalence lower than it would be without services, in less well-serviced areas, observed prevalence may be higher than it would otherwise be. Overall, then, we see no change. If the hypotheses receive support from the proposed research, then implications for service prioritisation and delivery could include a case for wider application of recovery-oriented practice. Critically, it would strengthen the case for action to correct inequities in the delivery of psychological treatments for depression in Australia so that combined psychological therapy and antidepressant medication, accessible and administered within an empowering framework, should be a nationally implemented standard.
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".