Airline passenger misconduct: management implications for physicians.
Bibliographic record
Abstract
OBJECTIVE: The history and etiology of airline passenger misconduct are discussed and relevant medico-legal and management implications reviewed. METHOD: The medical literature was reviewed and supplemented with internet searches for relevant information. Organizations including the Federal Aviation Administration, International Air Transport Association, Transportation Safety Board of Canada, and the Canadian Transportation Agency were contacted for unpublished information. Three cases of in-flight psychiatric emergencies in which two of the authors were involved are presented along with a review of relevant literature pertaining to the etiology and medical management of passenger misconduct. Recommendations for the in-flight management of disruptive passengers are discussed. RESULTS: Incidents of in-flight passenger misconduct represent a serious threat to passenger safety. The three cases presented highlight the difficulties involved in managing incidents of passenger misconduct in the context of limited resources and treatment options aboard aircraft. Ambiguity remains in regard to the responding physician's medico-legal obligations (and liabilities) during the management of an unruly passenger. However, liability risks appear minimal at this time. CONCLUSIONS: Awareness of the causes of passenger misconduct is required to adequately prevent, identify, and treat in-flight cases of passenger misconduct. Although most physicians will not be obligated to respond, liability issues do not appear to be a major factor preventing the offer of medical assistance.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".