Defining “Essential Digital Health for the Underserved”
Bibliographic record
Abstract
The World Health Organization envisions achieving "Health for All," to strive for equitable access to important health information and services to attain wellness (WHO 2023a). The COVID-19 pandemic reshaped the Canadian health system toward increasing digital health services, which improved access for some but underserved others. Integrating digital health into holistic health services delivery deserves careful consideration. This paper introduces the concept of "essential digital health for the underserved," by first defining the terms "digital health," "essential" and "underserved." Then, we share a summary of a discussion at a May 2023 conference with stakeholders, including patients, caregivers, health professionals, health policy makers, private sectors and health researchers. A series of papers follow to explore how digital health can help chart a responsible course for the future of essential digital health in Canada. In this post-pandemic era - with a health human resources shortage through attrition and retirement, an increased health service demand from patients and a greater strain on our recovering economy - innovative solutions need to be implemented to strengthen our Canadian health system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| 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".