A comparison of two methods of collecting economic data in primary care
Bibliographic record
Abstract
BACKGROUND: There have been few attempts to assess alternative methods of collecting resource use data for economic evaluations. OBJECTIVE: This study aimed to compare two methods of collecting resource use data in primary care: GPs' case records and a self-complete postal questionnaire. METHODS: 303 primary care attenders were sent a postal survey, incorporating a questionnaire designed to collect service utilisation information for the previous six months. Data were also collected from GP case records. The reporting of GP visits between the two methods, and estimates of costs associated with those visits, were compared. RESULTS: There was good agreement between the number of GP visits recorded on GP case records (mean 3.03) and on the CSRI (mean 2.99) (concordance correlation coefficient = 0.756). In contrast, estimates of average costs of visits from CSRI data were higher and had greater variance compared to case record-based costs (54.63 pound sterling versus 42.37 pound sterling; P = 0.003). This may be explained by differences in average visit length (11.66 versus 9.36 minutes). CONCLUSIONS: This study shows good agreement between GP case records and a self-complete questionnaire for the reporting of GP visits. However, differences in costs associated with those visits arose due to differences in the method used for calculating length of visit.
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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 | 0.193 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".