Using Foresight to develop eHealth intervention implementation strategy
Bibliographic record
Abstract
One of the key focus areas of the National Dementia Strategy, released by the Canadian government in 2019, is improving informal caregivers' quality of life through better support. While an array of services are available to support them, it’s usually up to caregivers to find them and navigating through a fragmented health and social support system can be challenging, time-consuming, frustrating, and often ineffective. \n \nInnovative approaches and eHealth interventions that can provide easy, timely, and need-based access to knowledge resources, enhances and safeguards care capacity among informal caregivers, reducing stress and depression levels, delaying nursing home placements, improving mood and their quality of life (Brodaty & Donkin, 2009). Innovations in technology are becoming a crucial element in improving support for and the well-being of family caregivers but \na number of social, cultural, ethical, and technical issues complicate the rapid emergence of new technologies which affects its adoption, implementation, and scalability. \n \nUsing a participatory foresight approach, this research project speculates futures, 15 years from now, to explore and envision an implementation model for eHealth services for informal Dementia caregivers in Ontario. At a time when technology innovations present significant challenges and opportunities, the purpose is to identify leverage points that will inspire and inform organizations, developers, researchers, healthcare providers, and innovators interested in translating knowledge into practice by designing sustainable and resilient eHealth interventions. This has been accomplished by understanding the needs of informal caregivers, implications of emerging technologies, and factors affecting implementation of eHealth solutions that support informal caregivers.
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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 | 0.061 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".