Health services utilization of people having and not having a regular doctor in Canada
Bibliographic record
Abstract
Canada having a universal health insurance plan that provides hospital and physician benefits offers a natural experiment of whether continuity of care actually provides lower or higher utilization of services. The question we are evaluating is whether Canadians, who have a regular physician, use more health resources than those who do not have one? Using two statistical methods, including propensity score matching and zero-inflated negative binomial regression, we analyzed data from the 2010 and 2007/2008 Canadian Community Health Surveys separately to document differences between people self-reportedly having and not having a regular doctor in the utilization of general practitioner, specialist, and hospital services. The results showed, consistently for all two statistical methods and two datasets used, that people reportedly having a regular doctor used more healthcare services than a matched group of people who was self-reportedly not having a regular doctor. For specialist and hospital utilization, the statistically significant differences were in the likelihood if the service was used but not in the number of specialist visits or hospital nights among users. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".