A Systematic Review of Assessment Methods for Seafarers’ Mental Health and Well-Being During the COVID-19 Pandemic
Bibliographic record
Abstract
Seafarers spend more time at sea than on land, which makes them a hard-to-reach community. Since their mental health and well-being is usually addressed from a land-based perspective, dedicated and validated methods incorporating maritime specificities are lacking. During the COVID-19 pandemic, research into seafarers' mental health and well-being flourished. However, a systematic review of the literature to assess the type and appropriateness of assessment methods pertaining to the mental health and well-being of seafarers has yet to be undertaken. This study reviews 5 databases (ERIC, Scopus, PubMed, Google Scholar and EBSCO) to assess the methods used to examine seafarers' mental health and well-being during the pandemic. Peer-reviewed literature alongside grey literature that applied quantitative or qualitative instruments to measure seafarers' mental health and/or well-being, published in English between March 2020 and February 2023, was eligible for the review. Studies from all geographic regions and regardless of nationality, rank and ship type of the subjects were explored. Database searches produced 272 records. Five additional records were identified via other methods. We identified 27 studies suitable for review, including 24 published in peer-reviewed scientific journals and 3 reports and surveys produced by the industry or welfare organizations. Assessment methods used to measure seafarers' mental health and well-being vary significantly in the literature. The frequent use of ad hoc questionnaires limits the possibility to replicate and compare the studies due to various inconsistencies. Furthermore, several validation and reliability measures needed more solidity when applied to the seafaring population. Such inadequate measuring and a mix of assessment methods impacted the comparison of results and might inflate the risks of underreporting or overstating mental complaints.
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 | 0.045 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".