Challenges and opportunities for policy development on digital health equity in four Canadian jurisdictions
Bibliographic record
Abstract
Digital health equity is an increasingly important topic, understood here as an aspirational state where everyone has access to the health-related digital technologies that support them in meeting their health-related needs. Despite strong emerging evidence regarding policy options to promote digital health equity, little policy action has been taken internationally to implement these options. The purpose of this paper is to report on a qualitative research project that explores the challenges and opportunities for policy development and implementation on digital health equity in four Canadian jurisdictions: Alberta, Saskatchewan, Ontario, and Quebec. We completed an Intersectionality-Based Policy Analysis, involving in-depth qualitative interviews with 23 participants, including both policy actors (i.e., those in positions to develop and/or implement digital health equity policy) and community leaders (i.e., those in positions advocating for the needs of structurally marginalized communities). Our findings illustrate a set of foundational policy options and more tailored policy programs for digital health equity, including the development of equity-focused accountability processes in new funding for digital health innovation. We also found challenges related to the political structure of Canada as a federation, and novel challenges related to the development of policy for digital health equity specifically. In our discussion, we explore three policy development challenges in detail: conflicting views on the priority status of health equity, challenges in building long-term partnerships with community for policy development, and conflicting views on the role of technology vendors in public policy for health care.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.059 | 0.017 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| 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".