Towards Equitable and Resilient Digital Primary Care Systems: An International Comparison and Insight for Moving Forward
Bibliographic record
Abstract
OBJECTIVE: While the COVID-19 pandemic provided a global stimulus for digital health capacity, its development has often been inequitable, short-term in planning, and lacking in health system coherence. Inclusive digital health and the development of resilient health systems are broad outcomes that require a systematic approach to achieving them. This paper from the IMIA Primary Care Informatics Working Group (WG) provides necessary first steps for the design of a digital primary care system that can support system equity and resilience. METHODS: We report on digital capability and growth in maturity in four key areas: (1) Vaccination/Prevention, (2) Disease management, (3) Surveillance, and (4) Pandemic preparedness for Australia, Canada, and the United Kingdom (data from England). Our comparison looks at seasonal influenza management prior to COVID-19 (2019-20) compared to COVID-19 (winter 2020 onwards). RESULTS: All three countries showed growth in digital maturity from the 2019-20 management of influenza to the 2020-21 year and the management of the COVID-19 pandemic. However, the degree of progress was sporadic and uneven and has led to issues of system inequity across populations. CONCLUSION: The opportunity to use the lessons learned from COVID-19 should not be wasted. A digital health infrastructure is not enough on its own to drive health system transformation and to achieve desired outcomes such as system equity and resilience. We must define specific measures to track the growth of digital maturity, including standardized and fit-for-context data that is shared accurately across the health and socioeconomic sectors.
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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".