Complaints and incident reports related to anaesthesia service are foremost attributed to nontechnical skills
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: While anaesthesiology is still perceived as a rather technical specialty, nontechnical skills of anaesthesiologists become increasingly important. In this context, we hypothesised that complaints and incident reports about anaesthesia service are often related to nontechnical skills. To test this hypothesis, we attributed complaints and incident reports to the seven roles of CanMEDS (Canadian Medical Educational Directives for Specialists), which are the role of 'medical expert', 'communicator', 'collaborator', 'health advocate', 'manager', 'scholar' and the role of 'professional'. METHODS: All complaints and incidents reported to the Anaesthesiology Department of the VU University Medical Centre Amsterdam (2001-2007) were analysed and attributed to the seven CanMEDS roles. RESULTS: In total, 169 reports could be identified, of which the majority were related to changes in operating room schedules (24%), teeth damage during laryngoscopy (9%), insufficient information about anaesthetic procedures (9%) or insufficient communication with other professionals (9%). Most reports were attributed to the roles of medical expert (39%) or manager (38%), followed by reports about the roles as professional (9%) and communicator (8%). CONCLUSION: Our data suggest an increased importance of nontechnical skills in addition to medical expertise in anaesthesia service. We propose to take this aspect into consideration in postgraduate training programmes of anaesthesiologists to improve satisfaction of patients as well as colleagues.
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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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".