A Study of Outpatient Utilization Between Widowers and Widows among the Elderly in Taiwan
Bibliographic record
Abstract
Both the numbers of aging and of widowed peers are gradually increasing in Taiwan. In this context, the aim of this paper is to compare outpatient services utilization from the perspective of predisposing, enabling and need characteristics between elderly widowers and widows. Subject data were obtained from the National Health Interview Survey of Taiwan in 2009. Among these data, 998 widowed persons aged 65 years old and over were analyzed. A Chi-square test and four different logistic regression models were used to investigate the influence of predisposing, enabling and need characteristics on outpatient utilization among elderly widowed persons. The empirical findings indicated that, for the predisposing characteristics, age showed significantly different outpatient utilization for widows. Nevertheless, there was a significant education gradient in outpatient utilization for elderly widowers. Next, for the enabling characteristics, economic status and national health insurance revealed significant effects on outpatient utilization for widows but not for widowers. In addition, for need characteristics, hypertension and kidney diseases played significant predictive factors related to outpatient utilization both for widows and widowers. However, other chronic diseases revealed significant differences on outpatient utilization only for widows. Finally, empirical results further indicated that widows were more likely to use outpatient services than widowers in later life. Therefore, study findings identified that predisposing, enabling and need characteristics were strongly correlated with the utilization of outpatient services for elderly widowers and widows. Administrators and managers could add the consideration of these three import characteristics into planning for the utilization of outpatient services, designing related policies, and allocation and use of health-care resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".