Improving interprofessional handover on labor and delivery: A needs assessment study
Bibliographic record
Abstract
Handover is the transfer of important clinical information between health providers. The current report describes a needs assessment of interprofessional labor and delivery handover at an urban hospital in Canada. The goal of this study was to explore the perceptions of the current handover meeting and opportunities for improvement. Using a constructivist paradigm, we conducted 28 semi-structured inter- views with handover participants. We used a recruitment grid to ensure we included the voices of participants representing each profession involved in interprofessional handover meetings. An inductive process was used to code the interview transcripts and theme the data. Major themes identified were: (1) Interprofessional handover contributes positively to team situational awareness, interprofessional relationships, and team communication; (2) Handover could be better if it had a more defined process; (3) Interprofessional handover can lead to feelings of intimidation; and (4) Interprofessional handovers on the labor and delivery unit in our setting need increased inclusivity of midwives. From these themes, continued development of interprofessional handover meetings on labor and delivery should be aimed at a formal definition of the scope and process for these meetings reducing feelings of intimidation, increased integration of Midwifery providers, and continued improvement of relationships between different professions and practitioners on labor and delivery.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".