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Record W2617469619 · doi:10.1027/2192-0923/a000109

Survey of Attitudes Toward Aviation Safety Management System (SMS) Training

2017· article· en· W2617469619 on OpenAlexaff
Suzanne K. Kearns, Julie Aitken Schermer

Bibliographic record

VenueAviation Psychology and Applied Human Factors · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsWestern UniversityUniversity of Waterloo
Fundersnot available
KeywordsCivil aviationAviationAviation safetyTraining (meteorology)Flight trainingPerceptionApplied psychologyScale (ratio)PsychologyInternational airportMedical educationEngineeringTransport engineeringGeographyMedicineFlight simulatorSimulation

Abstract

fetched live from OpenAlex

Abstract. With a growing volume of traffic, the aviation industry is moving to fully embrace a predictive approach to safety management, which requires the implementation of safety management system (SMS) training on an international scale. An online survey was distributed through an International Civil Aviation Organization (ICAO) State Letter to solicit perceptions of SMS training from a variety of international aviation professionals. The survey collected 1,103 complete responses. The results identified robust differences in how SMS training is perceived by men and women and by professionals from different geographic regions. Female respondents had more negative attitudes toward training than did males. Regarding regional differences, Middle Eastern participants had the most positive attitudes while Europeans reported the most negative attitudes toward SMS training. The data suggest caution is warranted before global distribution of SMS training and illustrate the importance of a learner analysis, as individual differences among learners may impact the effectiveness and adoption of SMSs.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.259
GPT teacher head0.513
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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