Experiences of Health Care Professionals Working Extra Weekends to Reduce COVID-19–Related Surgical Backlog: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: During the quiescent periods of the COVID-19 pandemic in 2020, we implemented a weekend-scheduled pediatric surgery program to reduce COVID-19-related backlogs. Over 100 staff members from anesthesiologists to nurses, surgeons, and administrative and supporting personnel signed up to work extra weekends as part of a novel weekend elective pediatric surgery program to reduce COVID-19-related backlog: Operating Room Ramp-Up After COVID-19 Lockdown Ends-Extra Lists (ORRACLE-Xtra). OBJECTIVE: In this study, we sought to evaluate staff perceptions and their level of satisfaction and experiences with working extra scheduled weekend elective surgical cases at the end of the 3-month pilot phase of ORRACLE-Xtra and identify key factors for participation. METHODS: Following the pilot of ORRACLE-Xtra, all perioperative staff who worked at least 1 weekend list were invited to complete an online survey that was developed and tested prior to distribution. The survey collected information on the impact of working weekends on well-being, overall satisfaction, and likelihood of and preferences for working future weekend lists. Logistic regression was used to estimate the association of well-being with satisfaction and willingness to work future weekend lists. RESULTS: A total of 82 out of 118 eligible staff responded to the survey for a response rate of 69%. Staff worked a median of 2 weekend lists (IQR 1-9). Of 82 staff members, 65 (79%) were satisfied or very satisfied with working the extra weekend elective lists, with surgeons and surgical trainees reporting the highest levels of satisfaction. Most respondents (72/82, 88%) would continue working weekend lists. A sense of accomplishment was associated with satisfaction with working on the weekend (odds ratio [OR] 19.97, 95% CI 1.79-222.63; P=.02) and willingness to participate in future weekend lists (OR 17.74, 95% CI 1.50-200.70; P=.02). Many (56/82, 68%) were willing to work weekend lists that included longer, more complex cases, which was associated with a sense of community (OR 0.12, 95% CI 0.02-0.63; P=.01). CONCLUSIONS: Staff participating in the first 3 months of the ORRACLE-Xtra program reported satisfaction with working weekends and a willingness to continue with the program, including doing longer, more complex cases. Institutions planning on implementing COVID-19 surgical backlog work may benefit from gathering key information from their staff.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".