Bibliographic record
Abstract
OBJECTIVE Because having a regular medical doctor is associated with positive outcomes, this study attempted to determine the characteristics of Canadians without regular doctors so that alternative methods of delivering care to people with those characteristics can be studied. DESIGN Secondary data analysis of the National Population Health Survey using bivariate analyses and logistic regression. PARTICIPANTS A total of 15 777 respondents older than 20 years. MAIN OUTCOME MEASURES Responses to the question “Do you have a regular medical doctor?” and analysis of 11 variables covering demographics, health status, and lifestyle factors. RESULTS One in seven respondents did not have a regular doctor. Younger respondents, men, single people, poorer respondents, respondents who perceived themselves in better health, recent immigrants, those without confidants, and smokers were more likely not to have regular doctors. Comparing provinces, participants from Quebec were least likely to have regular doctors. CONCLUSION Primary care reform might need to consider alternative ways of providing care to certain people. Future primary care programs could be targeted to improve coverage of relatively underserviced people, particularly men, people on low incomes, those without confidants, and
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".