Age and Sex-Related Comparison of Telemedicine Service Utilization During the COVID-19 Pandemic: A Retrospective Analysis
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has led to increased utilization of telemedicine services. Methods A retrospective analysis of all non-hospital-based medical healthcare services in Ontario from November 2019 to June 2021 was collected from the Ontario Health Insurance Plan (OHIP) billing database. Only fee-for-service billings were included in the present analysis. Coincident COVID-19 cases were obtained from Public Health Ontario. Comparisons were made based on age bracket, sex, telemedicine and in-person care. Results Billings for telemedicine services in Ontario increased from $1.7 million in November 2019 to $64 million in April 2020 and the proportions reached a mean peak of 72% in April 2020 and declined to 46% in June 2021. A positive correlation was found between the use of telemedicine and COVID-19 cases (p = 0.05). The age group with the highest proportion of telemedicine use was the 10–20-year-olds, followed by the 20–50-year-olds (61 ± 9.0%, 55 ± 7.3%, p = 0.01). Both age groups remained above 50% telemedicine services at the end of the study period. There is a persistent sex preference for females to adopt telemedicine (females 54.2 ± 8.0%, males 47.9 ± 7.7%, ANCOVA p = 0.05) Conclusions The use of telemedicine services remains at a high level across groups, particularly the 10–50-year-olds. There are clear age and sex preferences for using telemedicine. Studying these differences may provide insights into how the delivery of non-hospital-based medicine has changed during the COVID-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".