An International Survey of Maintenance Human Factors Programs
Bibliographic record
Abstract
There are many international approaches to the regulation of human factors programs for aviation maintenance organizations. Transport Canada and the European Aviation Safety Agency have established specific regulations regarding maintenance human factors. The Federal Aviation Administration has not yet established regulations but, instead, has created guidance documents and developed voluntary reporting programs for maintenance organizations. The purpose of this study was to assess the status of human factors programs in airline maintenance organizations and independent maintenance and repair organizations. Questions focused on training, error management, fatigue management, and other human factors issues. An online link was sent via E-mail to 630 addresses. Of these, 414 respondents returned a valid questionnaire (i.e., defined as responding to at least one content item), which resulted in a response rate of 66%. A highly-experienced group (i.e., over 65% had 20 years in aviation maintenance) from more than 50 countries responded to the questionnaire. Results highlight the maintenance human factors strategies, methods, and programs that companies use to reduce human error.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".