Article Component of Statistics Canada Catalogue no. 82-003-X Health Reports Waiting time for medical specialist
Bibliographic record
Abstract
Background Waiting for specialist consultations can represent a substantial component of overall waiting time in the continuum of care. However, relatively little is known about the factors associated with how long patients wait for an initial specialist consultation. Data and methods The analysis is based on a subsample of 5,515 respondents aged 15 or older to the 2007 Canadian Community Health Survey who had consulted a specialist about a new condition in the previous 12 months and reported a waiting time. Multivariate logistic regression models were used to identify patient- and provider-related factors associated with waiting time. Results Female patients were less likely than male patients to see a specialist within a month. The nature of the new condition and the source of referral were signifi cantly associated with waiting time. Compared with those referred by a family physician, patients referred by another specialist or a health care provider other than a physician, or who did not require a referral, were more likely to have a shorter waiting time. For men, but not women, household income and immigrant status were associated with waiting time. Interpretation This analysis suggests that factors beyond medical need are associated with how long patients wait to see a specialist. More research could usefully explore decision-making and communication processes between primary care physicians and specialists to better understand how urgency is assessed, how patients are triaged for specialist consultations, and how these patterns differ among various groups of patients.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.045 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.327 | 0.222 |
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".