Medicare Benefits Schedule (MBS) Review Advisory Committee post-implementation review of MBS telehealth items: abolition of initial telehealth consultations for non-general practitioner specialists
Bibliographic record
Abstract
In 2022, the Australian Federal Minister for Health and Aged Care commissioned the Medicare Benefits Schedule (MBS) Review Advisory Committee (MRAC) to conduct a post-implementation review of MBS telehealth services, including settings of video and telephone consultations. The MRAC has made a series of administrative recommendations for telehealth practice that appear at cross-purposes to the evidence-base on medical consultations and that would limit patient access to medical specialist assessment in Australia. These recommendations particularly underestimate the role of telehealth in rural and remote Australia and did not take into account high patient satisfaction with telehealth assessment and treatment during the ongoing coronavirus disease 2019 (COVID-19) pandemic. They also appear to contradict the Medical Board of Australia's guidance on telehealth. On this basis, the recommendations for telehealth principles and abolition of reimbursement for telehealth for all initial non-general practitioner medical specialist consultations should be withdrawn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.200 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".