Overview of the National Mental Health Benchmarking Project
Bibliographic record
Abstract
OBJECTIVE: This paper provides an overview of the National Mental Health Benchmarking Project (NMHBP) which explored the value of benchmarking within the four main program areas of public sector mental health (general adult, child and adolescent, older persons and forensic). The NMHBP involved a series of forums which enabled participating organizations to benchmark themselves against each other, with a view to understanding variability in performance against a range of key performance indicators (KPIs). METHOD: Twenty-three mental health organizations took part in the NMHBP. The forums culminated in each mental health organization documenting its performance against relevant KPIs. The processes, impacts and outcomes of the NMHBP were evaluated via a multi-methods evaluation. RESULTS: There was considerable variability across program streams for many of the KPIs, much of which could be explained by contextual factors. Within program streams there was considerable intra-organization variability. Participants found the examination of intra-organization variability on particular indicators to be useful. CONCLUSIONS: The NMHBP has shown that benchmarking is possible. Managers and clinical leaders will need to champion benchmarking and highlight its utility in relation to quality improvement and service development if the accountability goals of the Fourth National Mental Health Plan are to be realized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.090 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".