Multinational comparison study of aircraft pilot healthcare avoidance behaviour
Bibliographic record
Abstract
BACKGROUND: US and Canadian pilots are required to meet medical standards to secure their active flying status, but a subgroup exhibit healthcare avoidance behaviour due to fear of loss of that status. This phenomenon has the potential to impact pilot health, aeromedical screening and aviation safety. No international comparison study of pilot healthcare avoidance currently exists between US and Canadian pilots. AIMS: To compare the rate and subtypes of healthcare avoidance behaviour secondary to fear for loss of flying status between US and Canadian pilots. METHODS: A comparison analysis of data collected during two independent, non-probabilistic, cross-sectional internet surveys including any individual certified to perform flying duties in the USA (US survey) or Canada (Canadian survey). RESULTS: There were 4320 US pilots and 1415 Canadian pilots who completed informed consent and 3765 US pilots and 1405 Canadian pilots were included in the results. There were 56% of US pilots who reported a history of healthcare avoidance behaviour compared to 55% of Canadian pilots (P = 0.578). A multivariable logistic regression that included age, pilot type and gender showed that US pilots were slightly more likely than Canadian pilots to report this behaviour (odds ratio 1.22, 95% confidence interval 1.06-1.4). CONCLUSIONS: Healthcare avoidance behaviour due to fear of loss of flying status has a relatively high prevalence in both US and Canadian pilot populations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".