Informal Caregiver Quality Profiles for Dementia Patients

Fit information from latent profile models analyzing quality of informal care for people with dementia. The dataset contains results from a cross-sectional survey of 213 patient-caregiver dyads recruited from clinics and communities in Wuhan, China, between July 2023 and July 2024. Researcher Chan Cai published the data on figshare under a CC-BY-4.0 license.

Use Cases

• Identify caregiver quality profiles (high, moderate, low) using latent profile analysis results.
• Model associations between profile membership and factors like caregiver monthly income, social support, and perceived overload.
• Validate the Exemplary Care Scale (ECS) cut-off score of 15 for screening low-quality care.
• Analyze the influence of pre-illness relationship quality and patient Activities of Daily Living (ADL) scores on care quality profiles.

Strengths

• Analysis based on 213 patient-caregiver dyads.
• Clear profile distribution percentages: high (24.41%), moderate (44.60%), low (30.99%).
• Validated ECS cut-off score with high sensitivity (0.993) and specificity (0.955).
• Data collection occurred within a defined one-year period (July 2023 to July 2024).

Limitations

• Small sample size of 213 dyads limits generalizability.
• Geographic bias: data collected solely from Wuhan, China.
• Dataset is very small (5.5 KB), likely containing only model fit statistics, not raw survey data.

Provenance

Source

Chan Cai via figshare.

Collection Method

Cross-sectional survey and latent profile analysis.

Time Range

Survey conducted between July 15, 2023, and July 14, 2024.

Freshness

Last updated on figshare in April 2026.

Geography

Wuhan, Hubei, China.