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Record W4383710059 · doi:10.1177/10398562231188265

Kintsugi: Comprehensive repair of Australia’s fractured psychiatric care system

2023· article· en· W4383710059 on OpenAlexaff
Jeffrey CL Looi, Tarun Bastiampillai, Stephen Allison, Steve Kisely

Bibliographic record

VenueAustralasian Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAttritionPrivate sectorPublic sectorBridge (graph theory)PandemicHealth careBusinessMedicineNursingCoronavirus disease 2019 (COVID-19)Public relationsPsychiatryPolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: We provide an update of the current challenges facing public and private psychiatric care sector in Australia, contextualised by international and national information on factors affecting health system performance. CONCLUSIONS: There are practical and sustainable repairs that may bridge the gaps between primary care, private psychiatrists, and the public psychiatric system. These are based upon foundations of better linkages, adequate infrastructure, improved social support, and reforming public and private sector workplaces to retain healthcare workers despite pandemic-related attrition. Professional organisations need to redouble their efforts as advocates to governments, in the media matrix, and the general public.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.038
GPT teacher head0.373
Teacher spread0.335 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

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