Engagement Among Diverse Patient Backgrounds in a Remote Symptom Monitoring Program
Bibliographic record
Abstract
PURPOSE: Previous randomized controlled trials have demonstrated benefit from remote symptom monitoring (RSM) with electronic patient-reported outcomes. However, the racial diversity of enrolled patients was low and did not reflect the real-world racial proportions for individuals with cancer. METHODS: This secondary, cross-sectional analysis evaluated engagement of patients with cancer in a RSM program. Patient-reported race was grouped as Black, Other, or White. Patient address was used to map patient residence to determine rurality using Rural-Urban Commuting Area Codes and neighborhood disadvantage using Area Deprivation Index. Key outcomes included (1) being approached for RSM enrollment, (2) declining enrollment, (3) adherence with RSM via continuous completion of symptom surveys, and (4) withdrawal from RSM participation. Risk ratios (RR) and 95% CI were estimated from modified Poisson models with robust SEs. RESULTS: Between May 2021 and May 2023, 883 patients were approached to participate, of which 56 (6%) declined RSM. Of those who enrolled in RSM, a total of 27% of patients were Black or African American and 67% were White. In adjusted models, all patient population subgroups of interest had similar likelihoods of being approached for RSM participation; however, Black or African American patients were more than 3× more likely to decline participation than White participants (RR, 3.09 [95% CI, 1.73 to 5.53]). Patients living in more disadvantaged neighborhoods were less likely to decline (RR, 0.49 [95% CI, 0.24 to 1.02]), but less likely to adhere to surveys (RR, 0.81 [95% CI, 0.68 to 0.97]). All patient populations had a similar likelihood of withdrawing. CONCLUSION: Black patients and individuals living in more disadvantaged neighborhoods are at risk for lower engagement in RSM. Further work is needed to identify and overcome barriers to equitable participation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".