Reflections on the role of less-than-comprehensive (exclusionary) private health insurance hospital products in the Australian healthcare system
Bibliographic record
Abstract
The number of people in Australia that are currently covered by a hospital private health insurance product continues to rise every quarter. In September 2010, for the first time since the introduction of the public universal social insurance scheme, Medicare, more than 10million persons in Australia are covered by private health insurance. Although the number of persons covered by private health insurance continues to grow, the quality and level of cover that members are holding is changing significantly. In an effort to limit premium rises and to reduce the benefits paid for treatment, private health insurers have introduced, and moved a large number of existing members to, less-than-comprehensive private health insurance policies. These policies, known as 'exclusionary' policies, are changing the dynamics of private health insurance in Australia. After examining the emergence and prevalence of these products, this commentary gives three different examples to illustrate how such products are changing the nature of private health insurance in Australia and are now set to create a series of policy issues that will require future attention.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".