Outcomes of social support programs in brain cancer survivors in an Australian community cohort: a prospective study
Bibliographic record
Abstract
This study evaluated the impact of social support programs on improving cancer related disability, neuro-cognitive dysfunction and enhancing participation (quality of life (QoL), social reintegration) in brain tumour (BT) survivors.Participants (n=43) were recruited prospectively following definitive treatment in the community.Each BT survivor received an individualised social support program which comprised: face-to-face interview for education/counselling plus peer support program or community education/counselling sessions.The assessments were at baseline (T1), 6 week (T2) and 6-month (T3) post-intervention using validated questionnaires: depression anxiety stress scale (DASS), functional independence measure (FIM), perceived impact problem profile (PIPP), cancer rehabilitation evaluation system-short form (CARES-SF), a cancer survivor unmet needs measure (CaSUN), McGill quality of life questionnaire (MQOL) and Brief COPE.Participants' mean age was 53 years (range 31-72 years), the majority were female (72%); median time since BT diagnosis was 2.3 years and almost half (47%) had high grade tumours.At T2, participants reported higher emotional well-being (DASS 'anxiety' and 'stress' subscales, p<0.05;FIM 'cognition' subscale, p<0.01), improved function (FIM 'motor' subscale, p<0.01) and higher QoL (CARES-SF 'global' score, p<0.05;MQOL 'physical symptom' subscale, p<0.05).At the T3 follow-up, most of these effects were maintained.The intervention effect for BT specific coping strategies emerged for the Brief COPE 'selfdistraction' and 'behavioural disengagement' domains, (p<0.05 for both).There were no adverse effects reported.A post-treatment social support program can improve physical and cognitive function and enhancing overall QoL of BT survivors.Social support programs need further evaluation and should be encouraged by clinicians within cancer rehabilitative services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".