Predictors of Referral for Specialized Psychosocial Oncology Care in Patients With Metastatic Cancer: The Contributions of Age, Distress, and Marital Status
Bibliographic record
Abstract
PURPOSE: This study examines the rate and prediction of referral for specialized psychosocial oncology care in 326 patients with metastatic GI or lung cancer. PATIENTS AND METHODS: Referral information was abstracted from medical records and hospital databases. Patients completed measures of psychosocial and physical distress and functioning. RESULTS: Routine referral occurred in 33% of patients, and in 42% and 44%, respectively, of those scoring high on measures of depression (Beck Depression Inventory [BDI]-II >or= 15) and hopelessness (Beck Hopelessness Scale >or= 8). Univariate analyses indicated that referral was associated with younger age, unmarried status, living alone, presence of more depressive symptoms, hopelessness, and attachment anxiety, and with less social support, self-esteem, and spiritual well-being (all P < .05). Among the significantly depressed (BDI-II >or= 15), 100% of those less than 40 years of age, but only 22% of those age 70 years or older were referred. Multivariate analyses indicated that referral was associated with younger age, unmarried status, and presence of more depressive symptoms. Moreover, increasing age was associated with a progressively lower likelihood of referral independent of the level of distress. CONCLUSION: Routine referral of patients with metastatic cancer for psychosocial oncology care was predicted by presence of more severe depressive symptoms, younger age, and unmarried status. The rate of referral progressively declined with each decade of age, even among those with significant distress. These findings are consistent with some aspects of Andersen's model of health care utilization. The extent to which referred patients represent those who are most likely to benefit deserves further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".