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Record W2082211466 · doi:10.3109/10398562.2010.540248

Benchmarking Child and Adolescent Mental Health Organizations

2011· article· en· W2082211466 on OpenAlexaff
Peter Brann, Garry Walter, Tim Coombs

Bibliographic record

VenueAustralasian Psychiatry · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBenchmarkingPerformance indicatorMental healthContext (archaeology)Benchmark (surveying)PsychologyProcess (computing)Process managementApplied psychologyBusinessComputer sciencePsychiatryMarketing

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper describes aspects of the child and adolescent benchmarking forums that were part of the National Mental Health Benchmarking Project (NMHBP). These forums enabled participating child and adolescent mental health organizations to benchmark themselves against each other, with a view to understanding variability in performance against a range of key performance indicators (KPIs). METHOD: Six child and adolescent mental health organizations took part in the NMHBP. Representatives from these organizations attended eight benchmarking forums at which they documented their performance against relevant KPIs. They also undertook two special projects designed to help them understand the variation in performance on given KPIs. RESULTS: There was considerable inter-organization variability on many of the KPIs. Even within organizations, there was often substantial variability over time. The variability in indicator data raised many questions for participants. This challenged participants to better understand and describe their local processes, prompted them to collect additional data, and stimulated them to make organizational comparisons. These activities fed into a process of reflection about their performance. CONCLUSIONS: Benchmarking has the potential to illuminate intra- and inter-organizational performance in the child and adolescent context.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.217
GPT teacher head0.533
Teacher spread0.316 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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