Examining the longitudinal influence of loneliness on healthcare utilization: evidence from Taiwan’s national health insurance data
Bibliographic record
Abstract
BACKGROUND: This study combines a nationally representative sample from Taiwan with four years of National Health Insurance (NHI) data to explore the distinctive impact of emotional and social loneliness on health service utilization, including outpatient visits for mental health, general outpatient visits, emergency room (ER) visits, and hospitalization. METHODS: Data were drawn from the 2015 Taiwan Longitudinal Survey on Aging (TLSA) and merged with participants' NHI records from 2015 to 2018. The analysis used logistic regression for binary outcomes and negative binomial regression for counts. RESULTS: Results show that higher emotional loneliness in 2015 was associated with increased outpatient mental health visits over time and more general outpatient visits within the same year. Conversely, higher social loneliness in 2015 reduced the likelihood of seeking ER care in 2015. CONCLUSIONS: By merging national data and distinguishing emotional from social loneliness, this study offers insights into their differential impacts on healthcare utilization among older adults in Taiwan. It emphasizes the importance of addressing loneliness to improve physical and mental well-being and optimize the effective utilization of healthcare resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".