Perspectives and Experiences of Policy Makers, Researchers, Health Information Technology Professionals, and the Public on Evidence-Based Health Policies: Protocol for a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Evidence-based health policy (EBHP) development is critical to the judicious use of public funds. EBHPs increase transparency, accountability, effectiveness, and efficiency of policies. Encouraging collaboration between researchers or knowledge producers and policy makers is important because both communities have distinct professional cultures, resulting in them working separately without understanding each other. Knowledge sharing is a complex process that requires understanding of cultural aspects that may reduce cultural differences and increase the use of common language. Health information technology (HIT) is a useful tool to increase knowledge translation, which may result in the transparent use of evidence and networking in developing EBHPs. Our vision is to leverage HIT tools for a better health system that includes digitalized, open source, evidence-based, and transparent ways for collaboration and development of robust mechanisms and for sharing of synthesized evidence with knowledge user-friendly forms. OBJECTIVE: The aim of this study is to develop a conceptual framework on Knowledge translation and health Information Technology for Transparency (KhITT) in policy making and EBHPs (ie, the KhITT framework). The framework will be informed by the views of four key stakeholder groups (ie, policy makers, knowledge producers, HIT professionals, and the public) toward EBHP. The informants may also describe practices that demonstrate the EBHP development process and suggest technology platforms to enable this process. METHODS: We propose an exploratory, descriptive qualitative study to take place in British Columbia, Canada, using in-depth semistructured interviews. To ensure data saturation and trustworthiness, we will use a nonprobability, purposive snowball sample of up to 15 eligible participants in each of the four stakeholder groups. We will analyze the data using content analysis. RESULTS: The KhITT framework focuses on various stakeholders' perspectives to better understand their perceived needs and priorities in identifying issues with EBHP, in order to make informed recommendations. Ethics approval has been obtained by the harmonized Behavioural Research Ethics Board at the University of British Columbia. We anticipate that we will complete data collection and analysis by December 2020. Preliminary results will be published in summer 2021. CONCLUSIONS: Our ultimate goal of this study is to develop a conceptual framework and describe the technology platforms that would enable the EBHP process. We anticipate that our rigorous content analysis will be able to produce insights and themes that are able to address our objectives, contribute to an in-depth understanding of the EBHP process within British Columbia, highlight all influential factors, explicitly disseminate and communicate the study results, identify issues with EBHP and provide informed recommendations to address them, and enhance efforts toward transparent EBHPs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16268.
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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 | 0.087 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".