Does Introducing Public Funding for Allied Health Psychotherapy Lead to Reductions in Private Insurance Claims? Lessons for Canada from the Australian Experience
Bibliographic record
Abstract
OBJECTIVE: Provincial and territorial governments are considering how best to improve access to psychotherapy from the current patchwork of programmes. To achieve the best value for money, new funding needs to reach a wider population rather than simply replacing services funded through insurance benefits. We considered lessons for Canada from the relative uptake of private insurance and public funding for allied health psychotherapy in Australia. METHOD: We analysed published administrative claims data from 2003-2004 to 2014-2015 on Australian privately insured psychologist services, publicly insured psychotherapy under the 'Better Access' initiative, and public grant funding for psychotherapy through the 'Access to Allied Psychological Services' programme. Utilisation was compared to the prevalence of mental disorders and treatment rates in the 2007 National Survey of Mental Health and Wellbeing. RESULTS: The introduction of public funding for psychotherapy led to a 52.1% reduction in private insurance claims. Costs per session were more than double under private insurance and likely contributed to individuals with private coverage choosing to instead access public programmes. However, despite substantial community unmet need, we estimate just 0.4% of the population made private insurance claims in the 2006-2007 period. By contrast, from its introduction, growth in the utilisation of Better Access quickly dwarfed other programmes and led to significantly increased community access to treatment. CONCLUSIONS: Although insurance in Canada is sponsored by employers, psychology claims also appear surprisingly low, and unmet need similarly high. Careful consideration will be needed in designing publicly funded psychotherapy programmes to prepare for the high demand while minimizing reductions in private insurance claims.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".