Patterns of social support among older adults with cancer and associations with patient-reported outcomes: A latent class analysis
Bibliographic record
Abstract
INTRODUCTION: Social support can play an important role in the care of older adults living with cancer. However, different patterns of social support, such as emotional, instrumental, informational, appraisal, and giving support need to be considered to facilitate adjustments to cancer. This study aimed to explore the distinct patterns of social support among older adults with cancer and examine the socio-demographic variables and patient-reported outcomes that may be associated with patterns of social support. MATERIALS AND METHODS: Data were used from 7,097 respondents from the Experience of Cancer Patients in Transition Study administered in 2016. Socio-demographic variables included sex, age, marital status, place of residence, and income, alongside patient-reported outcomes. Latent class analysis was used to identify distinct social support patterns. Multivariable multinomial regression models were then used to determine predictors of these latent classes. RESULTS: Three latent classes of social support were identified: "low," "moderate," and "high" emotional support. Having "high" emotional support did not necessarily mean patients had the highest levels of all social support attributes. For example, the "low" emotional support group exhibited the highest appraisal support (16 % of class members) and giving support (42 % of class members). While most socio-demographic variables were not significant predictors of the latent classes, statistically significant differences were found in emotional health. DISCUSSION: Assessing social support requires consideration of the different patterns of support, as the presence of one attribute (e.g., appraisal or giving support) does not ensure the coverage of others (e.g., emotional support). Comprehensive assessments of these varied support patterns are recommended to better address the psychological and emotional challenges associated with a cancer diagnosis and to inform subsequent interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".