Impact of Cancer-Related Virtual Visits on Travel Distance, Travel Time, and Carbon Dioxide (CO2) Emissions during the COVID-19 Pandemic in Manitoba, Canada
Bibliographic record
Abstract
CancerCare Manitoba (CCMB) introduced virtual visits at the beginning of the COVID-19 pandemic to replace many in-person visits. This study examines the impact of virtual visits for cancer care on travel distance, travel time, and carbon dioxide (CO2) emissions. We included all visits to CCMB for invasive and in situ cancers from 1 April 2020 to 31 December 2022. Data were extracted from CCMB’s electronic health record. The percentage of visits done virtually by month was reported by age, gender, cancer diagnosis, and regional health authority of residence. Postal codes for patients’ residences and clinic locations were converted into latitude and longitude values. Travel distance, travel time, and CO2 emissions associated with travel were estimated. The percentage of virtual visits was highest during the months when COVID-19 restrictions were present in Manitoba and represent more than 50% of such monthly visits. Virtual visits increased with age, were highest among men with urogenital cancer, and were lowest among northern Manitoba residents. The median travel time per visit ranged from 30 min in Winnipeg to 15 h in the Northern Region. The estimated travel distance saved varied from 420,000 to 750,000 km per month. Estimated travel time saved varied from 5500 to 9600 h per month. Estimated CO2 emissions prevented varied from 87 to 155 metric tons per month. Virtual care is an important tool for better supporting those living with cancer by substantially decreasing travel distance and time. Virtual care also contributes to reducing greenhouse gas emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".