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Record W4309928092 · doi:10.1177/10398562221140993

Commentary on the private practice implications of the Deed of Settlement in the Honeysuckle Health – NIB Australian-Competition-Tribunal-hearing

2022· article· en· W4309928092 on OpenAlexaff
Jeffrey CL Looi, Gary Galambos, William Pring, Stephen Allison, Tarun Bastiampillai, Steve Kisely

Bibliographic record

VenueAustralasian Psychiatry · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDeedBusinessAutonomyTribunalHealth careEnforcementSettlement (finance)LegislationMedicinePublic relationsLawPolitical scienceFinancePayment

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide a commentary on the implications of the Deed of Settlement in the Honeysuckle Health - nib Australian-Competition-Tribunal Hearing. This hearing has major implications in relation to the potential for a single dominant private-health-insurance buying-group to contract for medical-purchaser-provider-agreements that might limit the clinical autonomy of patients and psychiatrists. CONCLUSIONS: The Australian Competition and Consumer Commission (ACCC) authorised the formation of a joint buying-group for private-health-insurers in 2021 to provide collective contracting and related services to private-health-insurers and other healthcare-payers. A consequent legal challenge resulted in a Deed of Settlement on 18 July 2022 that for 5 years preserves doctor-patient autonomy in clinical decision-making, the medical gaps scheme, the transparency of contractual arrangements, and if clinical data of those insured are collected by HH-nib, it must be with the full informed consent of patients. However, there remain options for private-health-insurers to apply for formation of new buying-groups, as well as to collect data and profile the general public and insured patients using online programs. Vigilance on private-health-insurer buying-groups, and the potential for US-style managed-care is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.309
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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