16 Effectiveness of interventions for informal caregivers of people with end-stage chronic illness: a systematic review
Bibliographic record
Abstract
Introduction End-stage chronic illness impacts not only patients but also their informal caregivers. Informal caregivers experience many challenges associated with their caring role, which can affect their psychological, physical and emotional wellbeing. Despite this, guidance on how to support informal caregivers of patients with end-stage, non-malignant, chronic conditions is lacking; with little evidence available on effective psychosocial interventions to address the needs of this group. Aims This review will explore: a) What interventions exist for informal caregivers of adults with end-stage chronic illness? b) What evidence exists to support intervention effectiveness in improving psychosocial outcomes of informal caregivers? Method The review follows PRISMA guidelines and is registered on the international Prospective Register of Systematic Reviews (PROSPERO) [CRD42021279151]. Four databases: Medline, CINAHL, EMBASE and PsycINFO were searched for relevant articles up to October 2020. Included studies involve: (1) adult, informal caregivers of individuals with end-stage chronic illness, (2) interventions targeting improved psychosocial outcomes of caregivers, (3) assessment of intervention efficacy via a minimum of pre-and-post measures. Dissertations, conference abstracts and studies focused on: patient rather than caregiver outcomes; caregivers for children; qualitative outcomes, were excluded. Two reviewers screened studies for inclusion based on titles/abstracts and full texts with disagreements resolved by a third reviewer. Results The narrative data synthesis reflects study design, comparison group, participant characteristics, recruitment strategy, intervention components, framework used, and outcomes. Description of key findings will be presented via data extraction table. Conclusion This review explores interventions for informal caregivers of those with chronic illness at end-of-life. The review will elucidate the key components, underpinning theoretical frameworks, mechanisms and measures utilised in effective interventions to improve outcomes for caregivers. Impact This study begins to build a much needed evidence base to inform intervention development to address the needs of these informal caregivers.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".