Intensity of care and perceived burden among informal caregivers to persons with chronic medical conditions: a systematic review and meta-analysis
Bibliographic record
Abstract
Informal caregivers provide ongoing assistance to a loved one with a health condition. No studies have compared caregiving intensity and perception of burden across chronic medical conditions. Databases were searched from inception through 11 September 2020 to identify studies that included the Level of Care Index or the Zarit Burden Inventory (ZBI) among caregivers for people with chronic diseases. Pooled mean ZBI scores and 95% confidence intervals by medical condition were calculated using a random effects model and heterogeneity with I2. Ninety-seven included articles reported on 98 unique samples across 21 chronic diseases. No study used the Level of Care Index. Among 12 disease groups with more than one study, heterogeneity was too high (I2 range: 0–99.6%, ≥76.5% in 11 groups) to confidently estimate burden. The percent of studies rated high risk of bias ranged from 0% to 98%, but all external validity items were rated as high-risk in >50% of studies. Findings highlight the need for studies on caregiver burden to improve sampling techniques; better report sampling procedures and caregiver and care recipient characteristics; and develop a standard set of outcomes, including a measure of caregiving intensity. Systematic Review Registration: CRD42017080962IMPLICATIONS FOR REHABILITATIONThe amount of burden reported by caregivers to loved ones is associated with reduced physical and mental health.We found considerable heterogeneity in perceived burden reported by informal caregivers across different studies within disease groups, which is likely related to methodological issues, including sampling techniques.Health care providers who use research on caregiver burden should assess how representative study samples may be and exercise caution in drawing conclusions. The amount of burden reported by caregivers to loved ones is associated with reduced physical and mental health. We found considerable heterogeneity in perceived burden reported by informal caregivers across different studies within disease groups, which is likely related to methodological issues, including sampling techniques. Health care providers who use research on caregiver burden should assess how representative study samples may be and exercise caution in drawing conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".