Measuring Resource Utilization
Bibliographic record
Abstract
A variety of methods may be used to obtain costing data. Although administrative data are most commonly used, the data available in these datasets are often limited. An alternative method of obtaining costing is through self-reported questionnaires. Currently, there are no systematic reviews that summarize self-reported resource utilization instruments from the published literature.The aim of the study was to identify validated self-report healthcare resource use instruments and to map their attributes.A systematic review was conducted. The search identified articles using terms like "healthcare utilization" and "questionnaire." All abstracts and full texts were considered in duplicate. For inclusion, studies had to assess the validity of a self-reported resource use questionnaire, to report original data, include adult populations, and the questionnaire had to be publically available. Data such as type of resource utilization assessed by each questionnaire, and validation findings were extracted from each study.In all, 2343 unique citations were retrieved; 2297 were excluded during abstract review. Forty-six studies were reviewed in full text, and 15 studies were included in this systematic review. Six assessed resource utilization of patients with chronic conditions; 5 assessed mental health service utilization; 3 assessed resource utilization by a general population; and 1 assessed utilization in older populations. The most frequently measured resources included visits to general practitioners and inpatient stays; nonmedical resources were least frequently measured. Self-reported questionnaires on resource utilization had good agreement with administrative data, although, visits to general practitioners, outpatient days, and nurse visits had poorer agreement.Self-reported questionnaires are a valid method of collecting data on healthcare resource utilization.
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 | 0.025 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".