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Record W3212571711 · doi:10.1177/10398562211054660

The Emperor’s New Clothes: Head-to-Health Centres expand in the absence of evaluation of efficacy

2021· article· en· W3212571711 on OpenAlexaff
Jeffrey CL Looi, Tarun Bastiampillai, Stephen Allison, Steve Kisely

Bibliographic record

VenueAustralasian Psychiatry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMental healthEmperorGovernment (linguistics)ClothingMental healthcareMedicineHealth careNursingPublic relationsPsychologyPublic administrationPolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: To discuss concerns about the Australian federal government announcement of further funding expansion of the Adult Mental Health Centres (AMHCs), now known as Head-to-Health centres. CONCLUSIONS: The expansion of AMHCs prior to evaluation recapitulates the policy predicaments and perils of the headspace federal-infrastructure allied-health private-practice model. Comprehensive evidence-based mental healthcare planning and practice is needed, rather than stand-alone services of unclear efficacy. We describe the principles of such an approach based upon an evidence-based Health Needs Assessment.

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 imitation

Not 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.

metaresearch head score (Codex)0.161
metaresearch head score (Gemma)0.282
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.839
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1610.282
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0070.014
Open science0.0030.007
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0290.005

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 head0.082
GPT teacher head0.470
Teacher spread0.388 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreCommentary

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

Citations1
Published2021
Admission routes1
Has abstractyes

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