Implementing a Fetal Health Surveillance Guideline in Clinical Practice: A Pragmatic Randomized Controlled Trial of Action Learning
Bibliographic record
Abstract
AIMS: The aim of this study was to determine the effects of an Action Learning intervention on nurses' use of a fetal health surveillance (FHS) guideline during labor of women who were low risk on admission for delivery. METHODS: Using a pragmatic randomized controlled trial, nurses were randomized to Action Learning (n = 44) or Usual Care (n = 45). Low-risk women were assigned to either an Action Learning nurse (n = 122) or a Usual Care nurse (n = 148). Data on practices during an episode of care (nurses' FHS practices from admission through to delivery in low-risk women) were collected at three trial time points: 1 month prior, during 6 months, and 1 month following. Guideline adherence, women's perception of birth experience, and enablers and inhibitors to intermittent auscultation (IA) were collected. Multivariate logistic regression determined the variables (chosen by the nurses) that predicted Action Learning nurses' adherence to FHS practices. FINDINGS: Statistically significant change was not evident between nurses' rate of FHS practices in the Action Learning group compared with Usual Care (Δ6.8%, odds ratio [OR] 0.16, 95% confidence interval [CI] 0.84-2.83). Postpartum, women reported high satisfaction with no significant difference by study group. Two labor events, epidural and narcotic analgesia, most influenced guideline appropriate care (p = .000, OR -4.04; p = .000, OR = 2.89) within the experimental group. LINKING EVIDENCE TO ACTION: Despite lack of between-group significant changes in FHS practices, Action Learning nurses, who chose areas of practice that presented obstacles to their guideline adherence ability (epidurals and narcotics), significantly changed their FHS practices. Researchers need to consider whether practice is long-standing acceptance of the evidence by healthcare providers, and the provider's intentions for implementation effectiveness when choosing an implementation strategy. Supportive nurses, Doppler availability, and clear policies support adherence to an IA guideline. Deimplementation of ineffective practice is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".