Bibliographic record
Abstract
BACKGROUND: Due to their working conditions, seafarers often don't benefit from the same medical coverage than the onshore population. Therefore, seafarers and their relatives often need to locate health information by themselves. While the rise of the Internet has drastically transformed the way people can gather information, the availability of specific maritime health information online still need to be evaluated scientifically. We aim here to document of the characteristic of maritime health-related online information. MATERIALS AND METHODS: A web survey was performed, articulated on two complementary analyses. First, an overall analysis of websites related to maritime health compared to websites related to two other health areas relevant for the general population (dental health and otorhinolaryngology) used as control. Second, an analysis of the understandability and actionability of a series of Wikipedia articles related to pathologies relevant for seafarers using the Patient Education Materials Assessment Tool (PEMAT). RESULTS: Online resources associated with maritime health were sparse and difficult to locate. When compared to other medical fields, maritime health websites were extremely poor in displaying useful information for seafarers. Available online resources regarding specific diseases affecting seafarers were mainly not adapted for a general audience and scored poorly both in terms of understandability and of actionability. CONCLUSIONS: This study provides a general overview of the degree of adaption of online material related to maritime health to seafarers' potential needs. Considerably more efforts need to be made in order to provide controlled online materials to answer the health information needs of the seafarers and their relatives.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".