Systematic review of self‐management interventions for older adults with cancer
Bibliographic record
Abstract
AIM: The purpose of this systematic review was to determine the effectiveness of self-management interventions for older adults with cancer and to determine the effective components of said interventions. METHODS: We conducted a systematic review of self-management interventions for older adults (65+) with cancer guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. We conducted an exhaustive search of the following databases: Ageline, AMED, ASSIA, CINAHL, Cochrane, Embase, Medline, PsychINFO, and Sociological Abstracts. We assessed for quality using the Cochrane Risk of Bias tool and Down & Black for quasi-experimental studies, with data synthesized in a narrative and tabular format. RESULTS: Sixteen thousand nine hundred and eight-five titles and abstracts were screened, subsequently 452 full-text papers were reviewed by two independent reviewers, of which 13 full-text papers were included in the final review. All self-management interventions included in this review measured Quality of Life; other outcomes included mood, self-care activity, supportive care needs, self-advocacy, pain intensity, and analgesic intake; only one intervention measured frailty. Effective interventions were delivered by a multidisciplinary teams (n = 4), nurses (n = 3), and mental health professionals (n = 1). Self-management core skills most commonly targeted included: problem solving; behavioural self-monitoring and tailoring; and settings goals and action planning. CONCLUSIONS: Global calls to action argue for increased emphasize on self-management but presently, few interventions exist that explicitly target the self-management needs of older adults with cancer. Future work should focus on explicit pathways to support older adults and their caregivers to prepare for and engage in cancer self-management processes and behaviours.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".