Enrollment and Completion Characteristics for Novel Remote Delivery Modes of the Self-management Programs During the COVID-19 Pandemic: Exploratory Analysis
Bibliographic record
Abstract
BACKGROUND: In-person, evidence-based, peer-facilitated chronic disease self-management programs have been shown to be effective for individuals from a variety of backgrounds, including rural and minority populations and those with lower socioeconomic status. Based in social learning theory, these programs use group processes to help participants better manage their chronic disease symptoms and improve their quality of life. During the pandemic, these in-person programs were forced to rapidly transition to remote delivery platforms, and it was unclear whether doing so increased disparities within our rural population. OBJECTIVE: The objectives of this analysis were to ascertain self-management program enrollment and completion characteristics between 2 remote delivery platforms, as well as determine the individual level characteristics that drove enrollment and completion across delivery modes. METHODS: We analyzed enrollment and completion characteristics of 183 individuals who either enrolled in a self-management workshop delivered through a web-based videoconference (VC) system or through a traditional, audio-only conference call (CC) offered by our health care network between April and December 2020. Chi-square tests of association were used to describe the characteristics of and differences between groups. Logistic regression analysis was used to determine significant predictors of enrollment and completion. RESULTS: Those who enrolled in the VC platform were significantly likelier to be younger and college educated than those who enrolled in the CC platform. Those who completed a program, regardless of delivery mode, were likelier to be older and college educated than those who did not complete a program. Multivariate analyses indicated that of those enrolled in the CC platform, completers were likelier to not be enrolled in Medicaid. Among those enrolled in the VC platform, completers were older, college graduates, female, and likelier to have reported poorer health than those who did not complete the program. CONCLUSIONS: The transition of self-management programs to remote delivery modes, particularly to those that rely on VC platforms, revealed that certain demographic groups may no longer be able or willing to access the service. Efforts need to be made to increase engagement in remote self-management workshops. In addition, equivalent quality services that do not rely on a digital platform must continue to be offered in order to promote health equity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".