A preliminary casemix classification system for Home and Community Care Clients in Western Australia
Bibliographic record
Abstract
The objective of the study was to examine the feasibility of using routinely available assessment, Minimum Data Set (MDS), socio-economic, geographic and unit cost data to define a discrete number of clinically meaningful, costhomogeneous Home and Community Care (HACC) client groups. Participants included new and existing Western Australian (WA) HACC beneficiaries from 1 January to 31 September 2001. Seventy two HACC agencies from metropolitan and rural regions participated, which represented 29% of the sector. A total of 9,404 quarterly periods of care contributed to the exploratory classification analysis and 12,697 to the confirmatory analysis. The final structure contained nine terminal nodes, achieved an R 2 of 23.7%, and was robust to fluctuations in cost. Higher costs were associated with increased functional dependency and the need for clinical services. The classification is empirically grounded, simple and robust, and has a number of potential policy and practice applications. Further refinement is required to improve its suitability as a funding tool.
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 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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".