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Record W4324128610 · doi:10.1097/jom.0000000000002838

Health Care Avoidance Among Canadian Pilots Due to Fear of Medical Certificate Loss

2023· article· en· W4324128610 on OpenAlex
Parth Patel, William R. Hoffman, James K. Aden, Jason P. Acker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedical and Agricultural Research Studies
Canadian institutionsCanadian Blood ServicesUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsHealth careHobbyOccupational safety and healthMedicineCertificateMedical careMedical emergencyPsychologyFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Canadian pilots may avoid health care and report inaccurate medical information due to fear of medical invalidation. We sought to determine if health care avoidance due to fear of certificate loss exists. METHODS: We conducted an anonymous 24-item Internet survey of 1405 Canadian pilots between March and May 2021. Responses were collected using REDCap, and the survey was advertised through aviation magazines and social media groups. RESULTS: Seventy-two percent of respondents (n = 1007) have felt worried about seeking medical care because it may impact their career or hobby. Respondents participated in various health care avoidance behaviors with the most common being having actually avoided or delayed medical care for a symptom (46%, n = 647). CONCLUSION: Canadian pilots fear medical invalidation and consequently, avoid health care. This may be severely impacting aeromedical screening effectiveness.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.084
GPT teacher head0.381
Teacher spread0.297 · 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