Bibliographic record
Abstract
OBJECTIVES: This article, based on the Andersen model, describes patterns of consultation with general practitioners (GPs) and specialists among Canadians aged 18 or older. Associations with health status and other factors are examined. DATA SOURCE: Estimates are based on data from the 2005 Canadian Community Health Survey (CCHS), cycle 3.1. ANALYTICAL TECHNIQUES: Cross-tabulations were used to estimate the proportion of adult Canadians who had had a GP consultation, four or more GP consultations, or a specialist consultation in the previous year. Adjusted logistic regression models were used to examine factors associated with such consultations when the effects of health need were taken into account. MAIN RESULTS: In 2005, 77% of Canadians aged 18 to 64 and 88% of seniors reported that they had consulted a GP in the previous year; 25% and 44%, respectively, had done so four or more times; and 27% and 34% had consulted a specialist. Individual health need, as measured by the presence of chronic conditions and self-reported general and mental health, was a strong determinant of service use. However, when need was taken into account, physician consultations were independently associated with age, sex, household income, race, language, urban/rural residence and having a regular family doctor. Seniors aged 75 or older and rural residents had low odds of specialist consultations, but high odds of four or more GP consultations. Visible minorities and Aboriginal people had lower odds of reporting specialist consultations than did Whites.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.306 | 0.089 |
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".