Intermittent auscultation versus continuous fetal monitoring: exploring factors that influence birthing unit nurses’ fetal surveillance practice using theoretical domains framework
Bibliographic record
Abstract
Abstract Background Intermittent Auscultation (IA) is the recommended method of fetal surveillance for healthy women in labour. However, the majority of women receive continuous electronic monitoring. We used the Theoretical Domains Framework (TDF) to explore the views of Birthing Unit nurses about using IA as their primary method of fetal surveillance for healthy women in labour. Methods Using a semi-structured interview guide, we interviewed a convenience sample of birthing unit nurses throughout Ontario, Canada to elicit their views about fetal surveillance. Interviews were recorded and transcribed verbatim. Transcripts were content analysed using the TDF and themes were framed as belief statements. Domains potentially key to changing fetal surveillance behaviour and informing intervention design were identified by noting the frequencies of beliefs, content, and their reported influence on the use of IA. Results We interviewed 12 birthing unit nurses. Seven of the 12 TDF domains were perceived to be key to changing birthing unit nurses’ behaviour The nurses reported that competing tasks, time constraints and the necessity to multitask often limit their ability to perform IA (domains Beliefs about capabilities; Environmental context and resources). Some nurses noted the decision to use IA was something that they consciously thought about with every patient while others stated it their default decision as long as there were no risk factors (Memory, attention and decision processes, Nature of behaviour). They identified positive consequences (e.g. avoid unnecessary interventions, mother-centered care) and negative consequences of using IA (e.g. legal concerns) and reported that the negative consequences can often outweigh positive consequences (Beliefs about consequences). Some reported that hospital policies and varying support from care teams inhibited their use of IA (Social influences), and that support from the entire team and hospital management would likely increase their use (Social influences; Behavioural regulation). Conclusion We identified potential influences on birthing unit nurses’ use of IA as their primary method of fetal surveillance. These beliefs suggest potential targets for behaviour change interventions to promote IA use.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".