0708 PREDICTORS OF PERCEIVED FATIGUE: A SURVEY OF 1,566 COMMERCIAL AIRLINE PILOTS
Bibliographic record
Abstract
Fatigue is a matter of serious concern in airline operations, with long, irregular, and night shifts, time zone crossings, disruption of circadian cycles, and concomitant sleep deprivation as potential contributing factors. Of the few fatigue surveys that have been performed among commercial airline pilots, none has investigated, to our knowledge, the correlates and predictors of perceived fatigue. The present study aimed to identify factors associated with fatigue in commercial airline pilots. A total of 1,566 pilots from a commercial airline company filled out a survey including questions on pilots and aircraft characteristics, commuting mode, sleep quality (Pittsburgh Sleep Quality Index, PSQI), fatigue management strategies, and flight duty periods. Pilots also completed the Fatigue Severity Scale (FSS). A multiple linear regression was performed to identify factors associated with increased fatigue levels. The R2adjusted for the regression model explains 44% of the variance in fatigue levels (R2adjusted = 0.44;p<.001). The 7 factors associated with higher fatigue levels were: 1) higher daytime dysfunction (β=.42;p<.001); 2) poorer subjective sleep quality (β=.19;p<.001); 3) greater sleep disturbances (β=.12;p<.001); 4) greater sleep needs (β=.09;p<.001), and; 5) use of sleep medication (β=.05;p<.01) on the PSQI, as well as; 6) less frequent naps the day before a night flight while away from home (β=.07;p<.001) and; 7) sleep having occurred on the flight deck during the last pairing (β=.07;p<.001). Rank, number of hours of duty, number of duty periods, and number of days being on duty between 02:00 and 05:00 home base time were forced in the regression model to control for work conditions but did not significantly contribute to the model. The relationship between higher fatigue levels and sleep disturbances indicates that interventions targeting sleep improvement could be beneficial for pilots. Future studies should specify how fatigue levels vary with fatigue management strategies, both during layover and on the flight deck. This study was supported by a NSERC grant (CUI2I 430856-12) awarded to L.L. and D.B.B.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".