The DELIVER study; the impact of research capacity building on research, education, and practice in Dutch midwifery
Bibliographic record
Abstract
BACKGROUND: Few examples exist of research capacity building (RCB) in midwifery. As in other jurisdictions, at the turn of this century midwives in the Netherlands lagged in research-based practice. Dutch professional and academic organisations recognised the need to proactively undertake RCB. This paper describes how a large national research project, the DELIVER study, contributed to RCB in Dutch midwifery. METHODS: Applying Cooke's framework for RCB, we analysed the impact of the DELIVER study on RCB in midwifery with a document analysis comprising the following documents: annual reports on research output, websites of national organizations that might have implemented research findings, National Institute for Public Health and the Environment (RIVM)), midwifery guidelines concerning DELIVER research topics, publicly available career information of the PhD students and a google search using the main research topic and name of the researcher to look for articles in public papers. RESULTS: The study provided an extensive database with nationally representative data on the quality and provision of midwifery-led care in the Netherlands. The DELIVER study resulted in 10 completed PhD projects and over 60 publications. Through close collaboration the study had direct impact on education of the next generation of primary, midwifery care practices and governmental and professional bodies. DISCUSSION: The DELIVER study was intended to boost the research profile of primary care midwifery. This reflection on the research capacity building components of the study shows that the study also impacted on education, policy, and the midwifery profession. As such the study shows that this investment in RCB has had a profound positive impact on primary care midwifery in the Netherlands.
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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 | 0.191 | 0.330 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".