Abstracts of the Trainee Conference 2023, 6–7 July 2023, Leeds, UK
Bibliographic record
Abstract
This quality-improvement project aimed to assess how well Whang arei Hospital (Northland, New Zealand) theatre staff knew the names of their colleagues, and whether introducing named scrub hats improved this.We also aimed to assess whether theatre staff supported this as an intervention.This intervention elsewhere has been shown to improve work efficiency [1], and may engender trust and a strong work ethic.Furthermore, there is evidence that improved communication improves patient outcomes [2]. MethodsIn 2021, questionnaires were self-completed by operating theatre staff.These asked them if they were able to name the colleagues who worked with them on that day.Later that year, named scrub hats were then provided to the entire anaesthetic department, as well as to most theatre nurses, surgeons and anaesthetic technicians.Two months following this intervention, the same questionnaires were completed by staff to assess whether name recall had improved.Theatre staff were also asked on both occasions whether they supported the use of named scrub hats as a means to improve communication. ResultsThirty-eight staff members completed the initial questionnaires.Responses were from surgeons (n = 6), anaesthetists (n = 11), anaesthetic technicians (n = 7) and nurses (n = 14).Respondents knew 85% of colleagues by name in their particular theatre.Eighty-four per cent of respondents thought the named caps were a good idea.Twenty staff members completed the post-intervention questionnaires.Responses were from surgeons (n = 2), anaesthetists (n = 8), anaesthetic technicians (n = 6) and nurses (n = 4).Respondents knew 91% of colleagues by name in their particular theatre.Eighty-five per cent of respondents thought the named caps were a good idea.Nurses knew 100% of the names of their colleagues (compared to 95.6% prior).Anaesthetists' name recall increased from 76% to 95% following this intervention. DiscussionDespite being less than the desired 100%, name recall was significantly better than anticipated.This was likely due to the small size of Whang arei Hospital in comparison to similar projects undertaken in UK hospitals.Post-intervention, anaesthetists had their names recalled 100% of the time (note all anaesthetists had named hats).The scrub cap intervention is well supported by theatre staff.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".