Referral patterns and psychosocial distress in cancer patients accessing a psycho‐oncology counseling service
Bibliographic record
Abstract
OBJECTIVE: One in three cancer patients will experience significant psychosocial distress, yet less than 10% will seek formal counseling. Who are the patients accessing counseling and what are their presenting needs? The purpose of this study was to identify referral patterns and psychosocial distress in cancer patients newly referred to a psycho-oncology counseling service. METHODS: Consecutive new referrals were tracked over 1 year (n=361). On initial visit, 145 patients completed a demographic survey, Brief Symptom Inventory-18 (BSI-18), Cancer Coping Questionnaire and Medical Outcomes Study Social Support Survey. RESULTS: Approximately one in five newly referred patients never attended counseling, with a significant representation of men (p=0.016) and lung cancer patients (p=0.010). Of 361 referrals, 295 patients attended initial counseling, 259 were approached, and 145/259 (56%) completed the survey. Most were women (79%), urban-dwelling (73%), diagnosed with non-advanced cancer (72%), well-educated (68%) and married (56%); average age of 52 years (SD=12.3). Two most common diagnoses were breast (36%) and genitourinary (14%) cancers. A total of 59% were significantly distressed (BSI-18 global severity index T-score⩾63) with less available social support than non-distressed patients (p=0.022). Coping strategy use did not differ significantly between distressed and non-distressed groups. Two of five patients were not significantly distressed. CONCLUSIONS: Most cancer patients attending counseling are well-educated urban residing women, with significant psychosocial distress. Further research is needed to better understand barriers and appropriate screening methods for accessing counseling, as well as the needs of men, advanced Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".