A qualitative exploration of factors that influence the uptake of tuberculosis services by low-skilled migrant workers in Singapore
Bibliographic record
Abstract
INTRODUCTION: Singapore relies heavily on migrant workers to build its country and harbours a relatively large population of these workers. Importantly, tuberculosis (TB) remains a pernicious threat to the health of these workers and in line with the United Nations High-Level Meeting in 2023, this paper aims to uncover the qualitative discourse facing migrant workers' uptake of TB services and provide policy recommendations to enable more equitable access to TB services for this population. METHODS: In-depth interviews were carried out with the migrant worker population recruited from a non-governmental organisation in Singapore that serves migrant workers through the provision of primary healthcare services, counselling, and social assistance. Interviews stopped once thematic saturation was achieved and no new themes and subthemes were found. RESULTS: A total of 29 participants were interviewed, including 16 Bangladeshis and 13 Chinese, aged between 22 and 54 years old, all worked in the construction sector. Four key themes emerged. They are (1) General TB knowledge: Misconceptions are prevalent, where we found that participants were aware of the disease but did not possess a clear understanding of its pathophysiology and associated health effects, (2) Contextual knowledge and perception of associated policies related to TB in Singapore: low awareness among migrant workers as participants' accounts depicted a lack of information sources in Singapore especially on issues related to healthcare including TB, (3) Attitude to towards TB: Motivation to seek treatment is underpinned by ability to continue working and (4) Stigma: mixed perception of how society views TB patients. The gaps identified in migrant workers' TB knowledge, their attitude towards the disease and their perception of the availability of TB-related services is despite Singapore's efforts to curb community spread of TB and its proactive initiatives to reduce the prevalence. CONCLUSION: Our study illuminates the various aspects that policymakers need to home in on to ensure this vulnerable group is sufficiently supported and equitably cared for if they develop active TB during their stay in Singapore as they contribute to the nation's economy. Leveraging the COVID-19 pandemic as a window of opportunity to improve overall healthcare access for vulnerable groups in Singapore can be a starting point.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".