Training needs among maritime professionals: a cross sectional study
Bibliographic record
Abstract
BACKGROUND: Maritime medical practice includes assessment of fitness, management of medical emergencies and healthcare on board and ashore. A better response to seagoing professionals' health requirements can be achieved when all the respective stakeholders have a common understanding. Training is a powerful tool to raise awareness and in particular continuing professional development is very significant in sustaining competencies of these professionals. The objective of the study was to identify maritime professionals' perceived training needs. MATERIALS AND METHODS: A cross-sectional study was conducted among maritime professionals participated in the 14th International Symposium on Maritime Health. Fifty responses with the response rate of 42.7% were received with medical doctors representing 78% of the sample. Descriptive statistics were used to describe the basic characteristics of the data needs using STATA 15.1. RESULTS: Among the 23 themes, the ranking of perceived training needs was highest for fitness evaluation and examination guidelines and working conditions (both with the same percentage 86%), onboard medicine 82%, rules and regulations and health and safety at work (with the same percentage 80%). The lowest was on gender issues 32%. CONCLUSIONS: The finding suggests the planning and effective implementation of further training for the maritime health professionals in a variety of topics including financing and management issues. Highest importance of training was expressed by those over 40 years and by medical doctors with more than 10 years of practice. These findings could usefully be combined with a qualitative study to gain in-depth results and may help the respective authorities to organise relevant training.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".