Using Google Trends to track walk-in clinic and emergency department searches over time and across provinces in Canada (Preprint)
Bibliographic record
Abstract
BACKGROUND Access to primary care is a challenge for many Canadians. Models of primary care vary widely across provinces, including arrangements for same day and after-hours access. Use of walk-in clinics and emergency departments may also vary, but data sources that allow comparison are limited. OBJECTIVE We use Google Trends to examine searches for walk-in clinics and emergency departments across provinces and over time in Canada, and compare results to other information about primary care access. METHODS We developed search strategies to capture the range of terms used for walk-in clinics (e.g. urgent care clinic, after-hours clinic) and emergency departments (e.g. ER, emergency room) across Canadian provinces. We used Google Trends to determine the frequencies of these terms relative to total search volume, and standardized search frequencies to allow comparisons across provinces and over time (2011-2018). We explored how care seeking captured by Google Trends correlates with other sources of data on primary care access by province. RESULTS Manitoba, British Columbia, and Nova Scotia had highest search frequency for emergency departments, and Saskatchewan, Alberta, and Ontario had the lowest. Searches for walk-in clinics were most common in the western provinces of British Columbia, Alberta, and Saskatchewan. Relative search frequency for walk-in clinics increased steadily, doubling in most provinces between 2011 and 2018. Higher search frequency for walk-in clinics was correlated with ability to get a same or next-day appointment and inversely correlated with both ED use for conditions treatable in patients’ regular place of care and having a regular medical provider. Emergency department searches were not correlated with survey data. CONCLUSIONS Search frequencies may reflect patient care seeking but may also be impacted by news coverage and other events, especially in the case of emergency department searches. We observe substantial interprovincial variation, and marked growth in the frequency of searches for walk-in clinics. Google Searches for walk-in clinics correlate with other measures of access, and appear to correspond to differences in policies related to walk-in clinics, advanced access, and after-hours care between provinces.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.030 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".