How Do the Determinants of Demand for GP Visits Respond to Higher Supply? An Analysis of Grouped Counts
Bibliographic record
Abstract
Summary Although there is a substantial literature on the determinants of demand for primary care, few studies have been able to examine how these determinants respond to higher supply. Some demand studies include supply variables or regional dummy variables to allow for different supply conditions. A few have tested for marginal effects of supply variables attributed at a highly aggregated geographic level. However, relatively little is known about whether there is a supply constraint and how demand responses differ across population groups. We used information from a household survey of 60,806 individuals for whom we had detailed information on supply and access conditions. As in many surveys, the annual measure of utilisation is a grouped count and we estimate a grouped negative binomial model (NegBin2) of the determinants of demand for general practitioner (GP) visits by Maximum Likelihood. We exploit a variable on which respondents were asked to report the convenience with which they were able to access GP services. We demonstrate the significance of this variable in determining the number of GP visits. We then examine which demand determinants are correlated with reported convenience. Finally, we compare the demand equations for respondents reporting unconstrained access to GPs with respondents reporting constrained access. We find that being unemployed has a significant positive effect on GP visits for individuals who reported poor access. People who own a car and reported a good access to GPs have significantly higher visits.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".