Measuring health system resource use for economic evaluation: a comparison of data sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A key challenge for evaluators and health system planners is the identification, measurement and valuation of resource use for economic evaluation. Accurately capturing all significant resource use is particularly difficult in the Australian context where there is no comprehensive database from which researchers can draw. Evaluators and health system planners need to consider different approaches to data collection for estimating resource use for economic evaluation, and the relative merits of the different data sources available. This paper illustrates the issues that arise in using different data sources using a sub-sample of the data being collected for an economic evaluation. Specifically, it compares the use of Australia's largest administrative database on resource use, the Health Insurance Commission database, with the use of patient-supplied data. The extent of agreement and discrepancies between the two data sources is investigated. Findings from this study and recommendations as to how to deal with different data sources are presented.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it