‘Nothing about us Without us’ – Development of a Patient- centered Digital Health Self-care Program for Marginalized, Underserved Populations with Heart Failure
Bibliographic record
Abstract
Heart failure (HF) is a global pandemic affecting over 26 million people worldwide. In high-income-countries (HICs), the use of evidence-based self-care models has led to a decline in HF prevalence. However, these improvements are less evident in low-and-middle-income countries (LMICs) and marginalized subgroup populations in HICs, like the Indigenous People, due to factors related to poor disease control and the disparities in a population’s social determinants of health. With the inequitable distribution of health services, digital health has been offered as an avenue to assist populations with limited resources. While mobile phones have become increasingly utilized, many digital health interventions have failed to be adopted, as they have been designed for usage in high-resource settings. With the current mismatch between technological innovation and local community priorities, this research sought to 1) evaluate the state of heart health within the remote communities in Northern Ontario and Northern Uganda, 2) investigate the contextual requirements to design of a community-based digital health program, and 3) adapt the program according to the identified design requirements. In Study 1a, we established that Indigenous communities and LMICs valued the use of digital tools, but its adoption would be dependent on its cultural compatibility. To better understand how cultural context should be integrated within digital tool design, Study 1b explored how various community engagement strategies could be utilized. Using these findings, in Study 1c, a research partnership was established with each community. In Study 2, a community-based needs assessment was conducted to evaluate the contextual influencers impacting community heart health. In Study 3, we developed a series of design requirements focused on empowering existing community resources and cultural values. In a society where the distribution of wealth is heavily unbalanced, there is concern that the digital divide will compound the effects of socioeconomic divisions. While the COVID-19 pandemic triggered the momentum for digital health, populations with a history of being overlooked, continue to be left with minimal support. As such, to close the gap associated with the digital divide, interventions need to be designed in reflection of the contextual circumstances contributing to a population’s poorer health outcomes.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".