Experiences and Needs of Core Participants in Surgical Ward Rounds: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Surgical ward rounds (SWRs) are typically led by doctors, with limited involvement from key participants, including patients, family members, and bedside nurses. Despite the potential benefits of a more collaborative and person-centered approach, efforts to engage these stakeholders remain rare. OBJECTIVE: This qualitative exploratory study examined the experiences and needs of doctors, nurses, patients, and their relatives during SWRs as part of a Participatory Design process. METHODS: Data were collected through ethnographic field studies, focus groups with the healthcare providers, patients and relatives, and dyadic interviews conducted as part of home visits to patients and their partners after discharge. Field notes and interview data were analyzed using systematic text condensation. RESULTS: Lack of organization, traditional roles, and cultural norms compromised the quality, efficiency, and user experience of SWRs in multiple ways. SWRs were routine-driven, treatment-focused, and received lower priority than surgical tasks. Unpredictability resulted in unprepared participants and limited access for nurses, patients, and relatives to partake. CONCLUSIONS: The study identified a gap between the organizational and cultural frameworks governing the SWRs and the experiences and needs of key participants. Digital technologies were perceived as a potential solution to address some of these challenges.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".