Trustworthy Evidence to Support Quality Digital Healthcare Policy for Underserved Communities: What Needs to Happen to Translate Evidence into Policy?
Bibliographic record
Abstract
In this paper, we explore what is needed to generate quality research to guide evidence-informed digital health policy and call the Canadian community of patients, clinicians, policy (decision) makers and researchers to action in setting digital health research priorities for supporting underserved communities. Using specific examples, we describe how evidence is produced and implemented to guide digital health policy. We study how research environments must change to reflect and include the communities for whom the policy is intended. Our goal is to guide how future evidence reaches policy makers to help them shape healthcare services and how these services are delivered to underserved communities in Canada. Understanding the pathways through which evidence can make a difference to equitable and sustainable digital health policy is vital for guiding the types of research that attract priority resources.
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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 | 0.409 | 0.706 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.035 | 0.040 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".