Health Quality Improvement Using Instructional Communication and Teamwork Videos: An Outcome Study
Bibliographic record
Abstract
Many factors contribute to errors that occur during emergency Cesarean birth under general anesthesia. The Joint Commission of Accreditation of Health Care Organizations (JACO) reports that 70% of sentinel events in obstetric practice are attributable to errors in communication and teamwork. Our objective was to develop a video training module to address these deficiencies, and measure its effectiveness. A webbased learning resource was created using professionally made videos that depicted effective and non-effective communication/teamwork techniques in an obstetrical event. This resource could be accessed by a facilitator of small group sessions or by self directed learners. Obstetrical nurses watched this learning resource and were then debriefed by a facilitator to highlight examples of how human factors contribute to the evolution of adverse events. The knowledge and skills, as well as, perceptions of their own behaviors and of other health professionals in the team, were evaluated preand post intervention. The performance of a subgroup of participants in a high-fidelity simulation of an emergency Cesarean birth was assessed to measure the outcome of intervention. Ninety-five obstetrical nurses were given the pre-intervention questionnaires, and 52 completed the post-intervention questionnaires one year later. Participants had significantly higher scores post-intervention (M = 0.78, SD = 0.09) as compared to pre-intervention (M = 0.73, SD = 0.12; t(53) = ?3.07, p d = .47). Following intervention, participants were more conscious of the behaviors of those they worked with (t(51) = ?4.99, p d = ?0.66). Ten months after intervention, nurses indicated that they were able to identify challenges in teamwork and communication in their practice, and were more willing to speak up and be more assertive, and use strategies of conflict resolution and communication that they had learned. There was an improvement in performance of a sub-group of participant when assessed using a simulation scenario. The video web-based learning resource used in small group sessions effectively improved performance of obstetrical nurses as evaluated using questionnaires and high fidelity simulation. Future work will determine if the web-based version will be as effective in orienting new staff to the challenges of working in acute care obstetrical practice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".