A nurse-led intervention for carers of people with high-grade glioma: A case series of carers reporting high distress
Bibliographic record
Abstract
Background: Carers play an important role in supporting patients diagnosed with high-grade glioma (HGG). However, this experience is frequently distressing and many carers require support. Objectives: To describe unmet needs of highly distressed carers of people with HGG and recommendations and referrals made by a nurse to support them within the Care-IS trial. Methods: Descriptive case series. Carers of people with HGG in the Care-IS trial reporting severe anxiety and/or depression at baseline and/or 4 months and high distress at baseline (during chemoradiotherapy) and at 4 months were included. Carers completed the Partner and Caregiver Supportive Care Needs Scale and Brain Tumor Specific Unmet Needs Survey for carers at baseline, 2, 4, 6, and 12 months. Monthly nurse telephone assessments documented carers' needs, recommendations, and referrals made. Data are reported descriptively. Results: = 98). Each reported a moderate-high need at ≥1 timepoint for: financial support and/or travel insurance; making life decisions in uncertainty; information about cancer prognosis/likely outcome; and coping with unexpected treatment outcomes. Specific brain tumor unmet needs were: adjusting to changes in personality, mental and thinking abilities, and accessing government assistance. Nurses provided information about treatment, side effects, and practical support. Recommendations for clinical care and referrals to community-based services, and medical specialists were offered. Conclusions: Highly distressed carers have diverse support needs in many domains, which can change over time. Nurses were critical in identifying carers' needs, providing support, and making referrals. Carers' distress and needs require ongoing screening and management.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".