Food, housing, and transportation insecurities among cancer survivors with concomitant caregiving roles.
Bibliographic record
Abstract
476 Background: Cancer survivors (CSs) encounter significant challenges related to housing insecurity, food insecurity, and transportation, which are critical Social Determinants of Health (SDoH). As CSs’ life expectancy continues to rise, they start or resume caregiving roles, i.e., becoming dual-role CSs. Our prior study has shown that dual-role CSs experience a lower well-being status compared to their counterparts. However, it remains unknown whether the dual-role CS population faces additional social barriers. Methods: Adult CSs were obtained from the nationally representative cross-sectional database - the Health Information National Trends Survey (HINTS) 6 (March to November 2022). SDoH includes food insecurity, housing insecurities and transportation barriers. Self-reported comfort level when patients sharing their social barriers with health care providers was assessed. Chi-square tests were used to compare the weighted prevalence and logistic regression models were used to calculate adjusted odds ratio (aOR) and 95% confidence interval (CI) comparing dual-role vs non-caregiver CSs. All calculations were weighted using SAS 9.4, with a significant level of p < 0.05. Results: Among the 18,386,583 weighted CSs, 20.5% of them were dual-role CSs. Dual-role CSs reported higher rates of food insecurity, housing insecurity, transportation barriers, or at least one of these challenges, compared to non-caregiver peers (p < 0.05). After controlling for confounders (Table), dual-role CSs were 7.5, 6.0, 4.8 and 6.9 times more likely to report food, housing, transportation, and any of the above barriers than their non-caregiver peers, respectively. However, there was no significant difference in their comfort level in sharing these social challenges with healthcare providers after adjusting for confounders. Conclusions: Our findings shed light on the profound social challenges faced by dual-role cancer survivors. The fact that nearly half of these individuals report experiencing at least one barrier underscores the significant unmet social needs within this population. Our results strongly advocate for proactive measures, including active initiation and comprehensive assessments of social needs, to effectively support dual-role cancer survivors.[Table: see text]
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".