The Research Agenda for Perinatal Innovation and Digital Health Project: Human-Centered Approach to Multipartner Research Agenda Codevelopment (Preprint)
Bibliographic record
Abstract
BACKGROUND Digital health innovations provide an opportunity to improve access to care, information, and quality of care during the perinatal period, a critical period of health for mothers and infants. However, research to develop perinatal digital health solutions needs to be informed by actual patient and health system needs in order to optimize implementation, adoption, and sustainability. OBJECTIVE Our aim was to co-design a research agenda with defined research priorities that reflected health system realities and patient needs. METHODS Co-design of the research agenda involved a series of activities: (1) review of the provincial Digital Health Strategy and Maternity Services Strategy to identify relevant health system priorities, (2) anonymous survey targeting perinatal care providers to ascertain their current use and perceived need for digital tools, (3) engagement meetings using human-centered design methods with multilingual patients who are currently or recently pregnant to understand their health experiences and needs, and (4) a workshop that brought together patients and other project partners to prioritize identified challenges and opportunities for perinatal digital health in a set of research questions. These questions were grouped into themes using a deductive analysis approach starting with current BC Digital Health Strategy guiding principles. RESULTS Between September 15, 2022, and August 31, 2023, we engaged with more than 150 perinatal health care providers, researchers, and health system stakeholders and a patient advisory group of women who were recently pregnant to understand the perceived needs and priorities for digital innovation in perinatal care in British Columbia, Canada. As a combined group, partners were able to define 12 priority research questions in 3 themes. The themes prioritized are digital innovation for (1) patient autonomy and support, (2) standardized educational resources for patients and providers, and (3) improved access to health information. CONCLUSIONS Our research agenda highlights the needs for perinatal digital health research to support improvements in the quality of care in British Columbia. By using a human-centered design approach, we were able to co-design research priorities that are meaningful to patients and health system stakeholders. The identified priority research questions are merely a stepping stone in the research process and now need to be actioned by research teams and health systems partners.
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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 | 0.073 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".