Patient preferences, referral process, and access to specialized care. Is patient choice constrained?
Bibliographic record
Abstract
In most developed countries, patients have been encouraged to elect their preferred choice of health care provider. However, this is different for specialized care, where the patient’s referral could be defined as a two-stage decision process and their options are pre-selected by their general practitioner (GP). In this study, we estimate patient preferences while controlling for the pre-selection procedure, and we investigate whether patients are actively choosing their provider for cancer care. The French national hospital discharge database (Programme de Médicalisation des Systèmes d’Information, PMSI – MCO 2017) has been used for investigation. We estimated a multinomial choice model when choice sets are in fact unobserved, which is assumed to identify patient preferences, in a revealed preferences framework. Our findings provide evidence that patients consider factors other than distance to select their provider. The patient – hospital distance as well as the specialization profile of providers appears to be internalized in the pre-selection process, while patients rather consider waiting times, hospital quality, and other provider attributes to make their final choice. We also found that patients would be treated in higher-quality hospitals if they had the opportunity to choose among all available providers.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".