Framework for policymaking on self-management of health by older adults using technologies
Bibliographic record
Abstract
BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, the use of information and communication technologies (ICTs) to support care management exponentially increased. Governments around the world adapted existing programs to meet the needs of patients. The reactivity of governments, however, led to changes that were inequitable, undermining groups such as older adults living with chronic diseases and disability. Policies that align with recent developments in ICTs can promote better health outcomes and innovation in care management. A framework for policymaking presents potential for overcoming barriers and gaps that exist in current policies. OBJECTIVE: The goal of this study was to examine how well a provisional framework for policymaking represented the interactions between various components of government policymaking on older adults' self-management of chronic disease and disability using ICTs. METHODS: Through an online survey, the study engaged policymakers from various ministries of the government of Ontario in the evaluation and revision of the framework. The data were analyzed using simple statistics and by interpreting written comments. RESULTS: Nine participants from three ministries in the government of Ontario responded to the questionnaire. Overall, participants described the framework as useful and identified areas for improvement and further clarification. A revised version of the framework is presented. CONCLUSIONS: Through the revision exercise, our study confirmed the relevance and usefulness for a policymaking framework on the self-management of disease and disability of older adults' using ICTs. Further inquiries should examine the application of the framework to jurisdictions other than Ontario considering the dissociated nature of Canadian provincial healthcare systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.081 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".