Harnessing Digital Technology to Provide Research Evidence
Bibliographic record
Abstract
Clinical research is fundamental in acquiring evidence to improve healthcare. Digitalisation has enabled new opportunities for research. The ability to collect, store, process, and analyse vast amounts of data in structured and unstructured format supports both care processes and secondary use of collected data for generating research evidence. However, issues with data quality, the limitations of available technologies and infrastructure, as well as a lack of competence regarding context, substance, and data processing hinder the efficient and safe use of data for research. This may also lead to misinterpretations and unfounded conclusions. It is therefore important for all actors involved in collecting and using data to understand their role in these processes and have competence to critically analyse and systematically improve their part. Collaboration and co-creation between practitioners, researchers, and service users, among different disciplines and professions is needed to understand the perspectives, needs, risks, possibilities and contributions of all involved. This chapter discusses; 1) the role of nurses and midwives in generating data that enables research, 2) technologies available for nurse and midwifery scientists, and 3) how data is transformed to support evidence-based practice for better outcomes.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".