Changing the Use of Electronic Fetal Monitoring and Labor Support: A Case Study of Barriers and Facilitators
Bibliographic record
Abstract
BACKGROUND: Decreasing the use of continuous electronic fetal monitoring and increasing professional labor support for low-risk pregnancies are recommended by the Society of Obstetricians and Gynecologists of Canada. This study explored factors influencing the successful (and unsuccessful) introduction of an evidence-based fetal health surveillance guideline. METHODS: This qualitative case study was conducted at two tertiary and one community hospital. Data were collected in 14 clinician focus groups (51 nurses), followed by 8 interviews with nurse administrators and educators. Analysis of verbatim transcripts and unit records included coding and categorizing data to form profiles that were compared across hospitals. RESULTS: Implementation of the guideline recommendations in the hospital settings was affected by many different factors originating in the practice environment, with the potential adopters, and related to the characteristics of the guideline. The influences of these diverse factors interacted sometimes to magnify or counteract each other's effect. The physical setting, adopter concerns, and the medicolegal issues surrounding the guideline played critical roles in uptake. In addition, changes preceding the introduction of the recommendations, the institution's agenda, and nursing and medical leadership influenced the uptake of guideline recommendations. The number and experience of nurses in each setting and availability of equipment also affected guideline acceptance and use. CONCLUSIONS: When implementing best practice, it is important to identify organizational barriers to the change that will need managing by the appropriate level of administration in the organization. Careful tailoring of implementation interventions to the barriers originating with the potential adopters is also necessary. Be prepared for unanticipated effects.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".