Your voice matters during COVID-19: Evaluation of digital divides across a tertiary cancer center.
Bibliographic record
Abstract
e18768 Background: Virtual care (VC) and electronic patient-reported experience measures (ePREM) have been systemically adopted during the COVID-19 pandemic to facilitate continuity of cancer care and quality improvement. Digital divides, defined as differential access and benefit from these tools, may exacerbate existing health inequities among patients. We aimed to evaluate digital divides through ePREM access, use, and responses during the pandemic. Methods: Your Voice Matters (YVM), a provincially-mandated ePREM survey, was adapted to an online platform in September 2020 and emailed to patients after outpatient VC and in-person clinic visits at a tertiary cancer centre in Ontario. Age, gender, postal codes, and completed surveys from September-December 2020 were collated. Income was estimated using area-level averages from Statistics Canada 2016 census data. Socioeconomic status was mapped to area-level dimensions of the Canadian Index of Multiple Deprivation: residential instability (RI), economic dependency, ethnocultural composition (EC), situational vulnerability (SV). Higher factor scores per dimension correspond to greater marginalization. Two-sided Chi-squared and t-tests were used to compare demographics between VC and in-person patients with a significance threshold of p < 0.001. Generalized estimating equations logistic regression models were used to assess associations between patient satisfaction and visit type, as well as demographics. Results: YVM was emailed to 28% (10625/37835) of patients with a response rate of 21.8% (2321/10625). Mean and minimum income (x $10,000) were highest among responders (6.6, 1.5) compared to non-responders (6.3, 1.3) and those without email (6.2, 1.1). Comparing responders with VC (n = 549) and in-person (n = 1719) visits, the former had higher mean income (6.9 vs 6.5, p < 0.001) and lower mean EC factor score (0.2 vs 0.4, p < 0.001). Satisfaction with care received was not associated with visit type and satisfaction with VC logistics was not associated with demographics. Patients with higher EC scores were more likely to rate low satisfaction in “culturally appropriate” (OR 0.69, 95% CI: 0.57-0.85) and “respect” (OR 0.69, 95% CI: 0.57-0.85) domains. Patients with higher SV scores were more likely to rate low satisfaction in the “physical symptoms” (OR 0.69, 95% CI: 0.51-0.94) domain, while patients with higher RI scores were more likely to rate low satisfaction in the “physical” (OR 0.82, 95% CI: 0.69-0.98) and “emotional symptoms” (OR 0.88, 95% CI: 0.79-0.98) domains. Conclusions: VC patients had positive experiences with visit logistics across demographics and their satisfaction with care was comparable to that of in-person patients. However, VC use and YVM access, use, and responses were associated with income and socioeconomic status. Identifying populations vulnerable to digital health inequities will guide strategies to bridge the divides.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".