Bibliographic record
Abstract
BACKGROUND: What is the role of nursing in the digital health transformation of the 21st century? The answer to this critical question may rely on how prepared nursing is to enter into design processes associated with this evolution. AIMS: The purpose of this paper is to introduce foundational terminology and tools to support increased nursing participation in user-centred design. Situated within a six-step design process, this includes a new analytic framework combining the disciplinary expertise of computer science with the nursing methodology Interpretive Description. METHODS: The analytic framework and recommended research process were developed over the course of two projects each employing a similar collaborative mixed-methods design. Primary methodological drivers were drawn from the software development life-cycle and Interpretive Description in these digital health intervention studies. RESULTS: Using aspects of software development practice, an analytic framework was conceived as part of an interdisciplinary research process allowing nurses to integrate their disciplinary expertise in user-centred digital design. The framework allows nurses to parse collected data into a robust set of functional and non-functional requirements for software developers while still engaging in a fulsome interpretive analysis. CONCLUSION: There is a need for nursing to occupy a more significant role in the advancement of technology innovation in healthcare. However, a lack of familiarity with design-thinking and associated practical experience impedes nursing voices in this area. Tools and processes are introduced to enhance an existing nursing methodology as a means to extend our disciplinary design capacity.
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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 | 0.110 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".