Conceptualizing a Nursing Model for Integration of Patient Engagement Into Perinatal Digital Health Development and Quality Assurance: A Critical Interpretive Synthesis
Bibliographic record
Abstract
This study examines current assumptions of digital transformation research in the perinatal context and constructs a nursing model through a critical interpretive synthesis. Perinatal digital transformation research is discussed and found to be lacking grounding in nursing concepts; nursing theory was integrated by examining data through the lenses of Woman- and Family-Centered Care (Person-centered Perinatal Care) and by applying Donabedian's Frame of quality assurance into the conceptual matrix. Here, iterative data collection occurred, initially through a scoping review examining the nature and range of perinatal digital health systems. Purposive sampling of empirical studies was conducted to saturate the data pool with all four attributes of patient engagement (access, personalization, therapeutic alliance, and commitment). Participatory action theory supported an abductive stage of analysis and informed pragmatic construction of the model. The model encompasses: (1) person-centered intervention mapping; (2) integration of process evaluation through stakeholder and user consultation; and (3) co-creation during real-life testing. The steps of the model are constructed to align with best practices in participatory action research, while holding nursing models as the foundational theoretical basis. This grounding in nursing theory will support a nursing lens for future action research related to the development of perinatal digital health systems.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".