18 Joy at work: NSQIP in times of COVID
Bibliographic record
Abstract
Background Surgical Clinical Reviewers (SCR9s) are the heart of this program. They collect data and use critical thinking and clinical experiences as nurses to analyze, interpret and present data to the teams and stakeholders working to improve surgical outcomes. In 2019, a restructuring of the program in our health authority led to a reduction in staff and resources. The COVID pandemic in 2020 brought on an even greater sense of isolation and disintegration. Our already reduced staff was redeployed, which resulted in further feelings of overwhelm and loss of value amongst the SCRs. The Joy at Work project was conceptualized in Mar 2021 to address these issues. Objectives The objective was to improve SCR’s Joy at Work measures by March of 2022. Methods Several PDSA’s were done, testing and refining numerous change ideas including biweekly meetings, targeted education sessions, a WhatsApp group and planned social outings. Use of staff satisfaction surveys, team member attendance, and meeting participation metrics to measure. Results Results demonstrated a reduction in sick calls, increased meeting attendance, and greater member participation (figures 1 and 2). We turned the QI lens back onto ourselves, to explore ways to improve our experience of work during this very challenging time. Conclusions Next steps to take will be to expand this project to our clerks and our Surgeon Champions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".