Social Isolation Detection in Palliative Care using Social Network Analysis
Bibliographic record
Abstract
Social isolation is a serious public health issue that can lead to various mental and physical health problems for individuals and jeopardizes their life's quality. The issue is more critical for older adults and palliative patients who are already suffering from different diseases and lack some abilities for performing their daily tasks. Additionally, this situation worsens when the COVID-19 pandemic adds forced social isolation to people's lives worldwide. In this paper, we propose a framework for detecting social isolation in community-based palliative care networks. We look at the problem as an outlier detection in community-based social graphs. Hence, we map the network to an attributed weighted social graph. Consequently, each patient is linked to a set of informal and formal care providers. We define formulae and indices to extract the norm of the society in terms of structural connections and assign a value to each individual based on the quality and quantity of its connections. The structural indices and a set of quality of life features such as age, marital status, life satisfaction, and capabilities are then used to identify the isolated individuals. We analyze and evaluate the performance of our algorithm on real-life data obtained by the Windsor Essex Compassion Care Community (WECCC), as well as various synthetic social graphs.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".