Barriers to psychosocial oncology service utilization in patients newly diagnosed with head and neck cancer
Bibliographic record
Abstract
OBJECTIVES: While patients with head and neck cancer (HNC) are known to experience higher levels of anxiety and depression, they do not always use psychosocial oncology (PSO) services when available. This study aimed to investigate barriers to PSO service utilization in this patient population, with the goal of appropriately targeting outreach interventions. METHODS: A conceptual model based on the Behavioral Model of Health Services Use was tested in 84 patients newly diagnosed with a first occurrence of HNC followed longitudinally over 1 year, including variables collected through self-administered questionnaires, Structured Clinical Interviews for DSM (SCID-I), and medical chart reviews. RESULTS: Within the first-year post-diagnosis, 42.9% of HNC patients experienced clinical levels of psychological distress, with only 50% of these consulting PSO services (29% total). A logistic regression indicated that PSO utilization was increased when patients presented with advanced cancer (P = 0.04) and a SCID-I diagnosis of major depressive disorder, anxiety disorder, or substance use disorder (P = 0.02), while there was an inverse relationship with self-stigma of seeking help (P = 0.03); these variables together successfully predicted 76.3% of overall PSO utilization, including 90.6% of non-users. CONCLUSIONS: Future outreach interventions in patients with HNC could address stigma in an attempt to enhance PSO integration into routine clinical care.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".