Digital Impact: A Program Evaluation of the Digital Delivery of the Living with Stroke™ Program
Bibliographic record
Abstract
In response to the COVID-19 pandemic, organizations had to rapidly adapt stroke support services to provide digital delivery options. In the post-pandemic environment, there continues to be significant interest in maintaining digital delivery options. However, there is a gap in understanding the impact of digital programs and the potential barriers that digital program delivery may create. As community organizations seek to expand programming in a growing digital space, it is important to consider how to make programming as effective, accessible, and sustainable as possible. March of Dimes Canada (MODC) is a national non-profit organization that provides services to people with disabilities and their caregivers, including people who have experienced a stroke. In partnership with Heart and Stroke Foundation Canada, MODC delivered the digital Living with Stroke™ program between March 2022 and December 2023, reaching 400 participants. Living with Stroke™ is an education program for individuals who have experienced a stroke and their caregivers, that is co-facilitated by people with lived experience and MODC staff. The program underwent a two-year evaluation to measure program effectiveness and feasibility of digital delivery, as well as examine the impact of digital program delivery for people with disabilities and their caregivers. After 18 months of mixed methods data collection with participants with lived experience of stroke, the evaluation found that digital program delivery provides a safe environment for participants to learn and connect with others and improve health knowledge and confidence. The digital delivery decreased barriers for most participants allowing for timely, convenient, and efficient access to programming. Results highlighted the importance of peer support and the ability of digital programs to enable connections for people from different communities and circumstances, in a way that was not previously feasible. This presentation will highlight the impact of these outcomes and associated best practices for delivering digital programming to people with disabilities, underscoring how digital solutions can expand community organizations’ ability to deliver effective programming to their community.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".