Who Uses Telehealth? Setting a Usage Baseline for the Early Identification of Pandemic Influenza Activity
Bibliographic record
Abstract
OBJECTIVE: To describe Ontario Telehealth usage for respiratory complaints during normal (i.e., interpandemic) circumstances. METHODS: Descriptive analyses were conducted on symptom calls of a respiratory nature made to Ontario (Canada) Telehealth during a 25-month period. RESULTS: Approximately 300,000 calls were made during the period under study, peaking annually in January/February. Calls were above average during the weekend and Mondays (p<0.0001). All-ages consultation rate was 0.21/1,000 (range, 0.11-0.43). Standardized call rates suggested an inverse relationship between age and call rate (except for >65 years of age). During peak activity, weekly telehealth call rates were up to more than twice the weekly mean and up to four times as high as the lowest weekly rate. Highest call rate was for under 5 years old (158.4/1,000). Male rates exceed female call rates in younger age groups; the pattern reversed in older age groups. The relationship between income and call pattern showed that income and call patterns were (1) directly related for under 5 years old, (2) inversely related for callers aged 45 years and above, and (3) bimodal (higher call rates in both the highest and lowest income groups) for callers 5-44 years old. DISCUSSION: The advent of annual respiratory illness seasons under study here resulted in surge capacity. Data such as these can and should be used for exercises such as seasonal and pandemic forecasting. Also, recent pandemic experience has showed us monitoring both overall exceedances in usage and deviances from established demographic patterns could enhance existing routine surveillance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".