The Primary Care of Immigrant Workers and Their Associated Characteristics within A Taiwanese Fishing Community
Bibliographic record
Abstract
In Taiwan, immigrant workers play an important role in fisheries but they are easily ignored by society. The health problems and associated characteristics of immigrant workers in fisheries remain unclear. Descriptive and retrospective analyses were performed. Outpatient data were collected from a primary care clinic for six fishing villages in North Eastern Taiwan between 1 August 2016 and 31 July 2017. The data of immigrant workers was recorded and compared with that of natives. A total of 241 immigrant workers and 1342 natives were enrolled. Compared with the natives, the immigrant workers had a significantly younger age, male predominance, and fewer mean visits per year. The immigrant worker’s visits tended to be more highly focused during the third quarter of the year. Immigrant workers paid more registration fees and self-payment, but they paid less on diagnosis fees, oral medication, laboratory exams and had reduced total costs. The top five diagnoses for immigrant workers were respiratory diseases (38.3%), trauma (15.2%), musculoskeletal diseases (11.2%), skin-related diseases (9.5%), and digestive diseases (9.1%). Immigrant workers were positively correlated with infectious/parasitic diseases, and negatively correlated with medical consults and endocrine/metabolic diseases. Immigrant workers were also positively associated with registration fees and self-payment, but negatively correlated with diagnosis fees and total costs (all p < 0.05). The distribution of skin diseases and trauma were affected by age and sex as opposed to ethnic group. Immigrant status’ health issues should be given more attention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".