Evaluation of the Association Between Sociodemographic Status and Breast Screening Volumes During the COVID-19 Pandemic in a Provincial, Population-Based Organized Breast Screening Program
Bibliographic record
Abstract
Objectives We sought to evaluate the association between patient sociodemographic status and breast screening volumes (BSVs) during the COVID-19 pandemic in a large, population-based breast screening program that serves a provincial population of over 5 million. Methods: All patients who completed breast screening between April 1 st , 2017 and March 31 st , 2021 were eligible to participate. An average of 3 annual periods between April 1 st , 2017 and March 31 st , 2020 were defined as the pre-COVID period while the period between April 1 st , 2020 and March 31 st , 2021 was defined as the COVID-impacted period. The Postal Code OM Conversion File Plus was applied to map patient residential postal codes to 2016 census standard geographical areas, which provided information on community size, income quintile and dissemination areas. Dissemination areas were subsequently linked to the Canadian Index of Multiple Deprivation (CIMD). Results Overall BSV was reduced by 23.0% during the COVID-impacted period as compared to the pre-COVID period. Percent reductions in BSVs were greatest among younger patients aged 40 to 49 years (31.3%) and patients residing in communities with a population of less than 10,000 (27.0%). Percent reduction in BSV was greatest among patients in the lowest income quintile (28.1%). Percent reductions in BSVs were greatest for patients in the most deprived quintiles across all 4 dimensions of the CIMD. Conclusion Disproportionate reductions in BSVs were observed during the COVID-19 pandemic among younger patients, patients residing in rural communities, patients in lower income quintiles, and patients in the most deprived quintiles across all 4 dimensions of the CIMD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".