Patients seeking care during acute illness. Why do they not see their regular physicians?
Bibliographic record
Abstract
OBJECTIVE: To identify factors that predict whether patients prefer seeing their regular physicians and whether they do see their regular physicians during acute illness. DESIGN: Cross-sectional, population-based telephone survey. SETTING: Urban areas in southern Ontario. PARTICIPANTS: Random sample of 304 people who had regular physicians, insurance coverage, and had last seen a physician for acute illness. Of the 304, 256 (84.2%) preferred seeing their regular physicians during acute illness, and 48 (15.8%) did not. Of those who preferred seeing their regular physicians, 131 (51.2%) did see their regular physicians, 125 (48.8%) did not MAIN OUTCOME MEASURES: Preference for seeing regular physician and seeing regular physician during acute illness. RESULTS: Multiple logistic regression found that believing continuity of care was important and traveling further increased, while recent hospitalization and difficulty seeing physicians during or after office hours decreased, the likelihood of actually seeing their regular physicians. CONCLUSION: Almost half the patients who preferred seeing their regular physicians for acute illness did not actually see their regular physicians. Improving access to regular physicians might encourage patients to always try to see them.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".