Head and neck cancer patients want us to support them psychologically in the posttreatment period: Survey results
Bibliographic record
Abstract
OBJECTIVES: No study systematically has investigated the supportive care needs of general head and neck cancer patients using validated measures. These needs include physical and daily living needs, health system and information needs, patient care and support needs, psychological needs, and sexuality needs. Identifying the unmet needs of head and neck cancer patients is a necessary first step to improving the care we provide to patients seen in our head and neck oncology clinics. It is recommended as the first step in intervention development in the Pan-Canadian Clinical Practice Guideline of the Canadian Partnership Against Cancer (see Howell, 2009). This study aimed to identify: (1) met and unmet supportive care needs of head and neck cancer patients, and (2) variability in needs according to demographics, disease variables, level of distress, and quality-of-life domains. METHODS: Participants were recruited from the otolaryngology-head and neck surgery clinics of two university teaching hospitals. Self-administered questionnaires included sociodemographic and medical questions, as well as validated measures such as the Supportive Care Needs Survey-Short Form (SCNS-SF34), the Hospital Anxiety and Depression Scale (HADS), and the Functional Assessment of Cancer Therapy-General (FACT-G) and Head and Neck Module (FACT-H&N) (quality of life measures). RESULTS: One hundred and twenty-seven patients participated in the survey. 68% of them experienced unmet needs, and 25% revealed a clinically significant distress level on the HADS. The highest unmet needs were psychological (7 of top 10 needs). A multiple linear regression indicated a higher level of overall unmet needs when patients were divorced, had a high level of anxiety (HADS subscale), were in poor physical condition, or had a diminished emotional quality of life (FACT-G subscales). SIGNIFICANCE OF RESULTS: The results of this study highlight the overwhelming presence of unmet psychological needs in head and neck cancer patients and underline the importance of implementing interventions to address these areas perceived by patients as important. In line with hospital resource allocation and cost-effectiveness, one may also contemplate screening patients for high levels of anxiety, as well as target patients who are divorced and present low levels of physical well-being, as these patients may have more overall needs to be met.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".