Morbidity in expatriates—a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Expatriates comprise an important, but rarely studied subset of international travellers. This study was performed to assess the incidence of health events in an expatriate group and to evaluate factors affecting this incidence. METHODS: A cohort of 2020 Foreign and Commonwealth Office (FCO) staff and partners living abroad were followed-up over 1 year. The main outcome measure was incidence of illness or injury serious enough to require consultation with a doctor. Data collection was by means of a self-administered questionnaire. Poisson regression was used to estimate the rates of health events and to test for association between health events and a number of independent variables. RESULTS: The incidence of health events was 21%. Trauma (incidence 5%), musculoskeletal disorders (incidence 4%) and infectious disease (incidence 3%) were the principal causes of morbidity. The incidence of psychological disorders was low (1%). Of significance, employees were at increased risk of morbidity when compared to partners, with a higher incidence of health events [incidence rate ratio (IRR) 1.4, 95% CI 1.1-1.9] and psychological disorders (IRR 5.9, 95% CI 1.0-34.1). Moreover, unaccompanied employees were at increased risk of health events (IRR 1.3, 95% CI 1.0-1.7), and of traumatic injury (IRR 2.3, 95% CI 1.3-4.3) when compared to accompanied employees. CONCLUSION: While the morbidity in FCO personnel is low in comparison to other expatriate groups, the higher risk of morbidity in employees and unaccompanied individuals merits further research, particularly to ascertain whether work demands, isolation or risk-taking behaviour are contributory factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".