Quality of Life in Caregivers of Patients with Brain Tumours: A Systematic Review and Thematic Analysis
Bibliographic record
Abstract
Objective . (1) Examine QoL of caregivers of patients with brain tumours compared to population norms and other cancer caregiver groups, (2) appraise the content of quantitative QoL outcome measures utilised, and (3) assess to what extent QoL measures used in research align with caregivers’ priorities. Methods . Systematic literature search of studies including caregivers of brain tumour patients using self‐completed assessments of QoL. Extracted data from included studies included quantitative QoL outcome data, QoL outcome measures utilised, and the included QoL domains. The impact of brain tumour patient caregiving was assessed by summarising included data comparing brain tumour caregivers to other cancer caregivers and normative population data. QoL measures utilised by the studies and their domains were extracted, coded, and analysed by themes. The rates of investigation by theme were then compared to existing data on caregiver‐own preference in relation to QoL. Results . 49 studies, including 57 outcome measures, incorporating a combined 124 QoL domains. Brain tumour caregivers reported lower QoL outcomes than population norms but similar to other cancer caregiver groups. Thematic analysis of QoL domains generated 7 themes: caregiving burden and adaptation, existential and self, family and social support, finances, information needs, physical symptoms and functioning, and psychological symptoms and wellbeing. The most investigated themes were physical and psychological symptoms, the most important for caregivers themselves were family and social support. Conclusions . Caregiving for brain tumour patients is shown to negatively affect QoL, particularly mental health, burden, and social life. Existing QoL research in caregivers of brain tumour patients predominantly utilises generic QoL measures designed for use in patients and draws a medicalised view of QoL priorities. The few studies using caregiver‐specific QoL measures demonstrated closer alignment to caregiver preferences such as family and social support.
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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 | 0.033 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".