Unmet Social Needs Among Cancer Survivors Who Were Concomitant Caregivers: A Cross-Sectional Analysis of the Health Information National Trends Survey
Bibliographic record
Abstract
PURPOSE: This study aimed to examine the impact of social determinants of health (SDOH) barriers, including food, housing, and transportation insecurities, on cancer survivors who also serve as caregivers (dual roles), compared with cancer survivors only, caregivers only, and the general population. A secondary aim was to assess their comfort level in sharing these barriers with health care providers. METHODS: Data were obtained from the 2022 National Cancer Institute Health Information National Trends Survey (HINTS 6), which collected information on SDOH outcomes, including food, housing, and transportation insecurities, as well as participants' comfort level in sharing their SDOH concerns with providers. We compared these outcomes across four groups: dual roles, cancer survivors only, caregivers only, and the general population. Weighted multivariable logistic regression models were used to calculate the adjusted odds ratio (aOR) of SDOH factors by cancer survivor/caregiver status. RESULTS: Overall, 49.1% of dual roles reported facing at least one form of food, housing, or transportation insecurities. Dual roles were 4.61 (aOR, 4.61 [95% CI, 2.71 to 7.84]) and 9.45 (aOR, 9.45 [95% CI, 4.45 to 20.07]) times more likely to report one or more of the above SDOH barriers compared with the general population and cancer survivors only, respectively. However, dual roles did not appear to feel more comfortable in sharing their barriers with health care providers compared with other groups. CONCLUSION: Our study highlights the significant unmet needs of cancer survivors who also serve as caregivers, as they face higher levels of SDOH barriers than both the general population and cancer survivors only. However, they did not have a greater comfort level in sharing them with providers, underscoring the necessity for targeted strategies to address the unique challenges faced by this vulnerable population.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".