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Record W2035583771 · doi:10.3109/10398562.2011.578641

Benchmarking the Cost Efficiency of Community Care in Australian Child and Adolescent Mental Health Services: Implications for Future Benchmarking

2011· article· en· W2035583771 on OpenAlexaff
Gareth Furber, Peter Brann, Clive Skene, Stephen Allison

Bibliographic record

VenueAustralasian Psychiatry · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsBenchmarkingMental healthBenchmark (surveying)Best practiceMedicineHealth careCost databaseTotal costOperations managementPsychologyPsychiatryMarketingBusinessEconomicsAccountingManagementEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to benchmark the cost efficiency of community care across six child and adolescent mental health services (CAMHS) drawn from different Australian states. METHOD: Organizational, contact and outcome data from the National Mental Health Benchmarking Project (NMHBP) data-sets were used to calculate cost per "treatment hour" and cost per episode for the six participating organizations. We also explored the relationship between intake severity as measured by the Health of the Nations Outcome Scales for Children and Adolescents (HoNOSCA) and cost per episode. RESULTS: The average cost per treatment hour was $223, with cost differences across the six services ranging from a mean of $156 to $273 per treatment hour. The average cost per episode was $3349 (median $1577) and there were significant differences in the CAMHS organizational medians ranging from $388 to $7076 per episode. HoNOSCA scores explained at best 6% of the cost variance per episode. CONCLUSIONS: These large cost differences indicate that community CAMHS have the potential to make substantial gains in cost efficiency through collaborative benchmarking. Benchmarking forums need considerable financial and business expertise for detailed comparison of business models for service provision.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.193
GPT teacher head0.522
Teacher spread0.330 · 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.

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

Citations6
Published2011
Admission routes1
Has abstractyes

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