Using Exploratory Structural Equation Modeling to Examine Caregiver Distress and Its Contributors
Bibliographic record
Abstract
OBJECTIVES: To develop and test the direct and indirect associations between caregiver distress and its many contributing factors and covariates. DESIGN: Analysis using data from a national, cross-sectional survey of Canadian caregivers. SETTING AND PARTICIPANTS: A total of 6502 respondents of the 2012 General Social Survey-Caregiving and Care-receiving who self-identified as a caregiver. METHODS: We used exploratory structural equation modeling to achieve our aims. Based on literature review, we hypothesized a structural model of 5 caregiving factors that contribute to distress: caregiving burden, caregiving network and support, disruptions of family and social life, positive emotional experiences, and caregiving history. Survey items hypothesized to measure each latent factor were modeled using exploratory factor analysis (EFA). After establishing a well-fit EFA model, structural equation modeling was performed to examine the relationships between caregiving factors and caregiver distress while controlling for covariates such as caregiver's and care-recipient's sociodemographic characteristics and kinship. RESULTS: EFA established a well-fit model that represented caregiver distress and its 5 contributing factors as hypothesized. Although all 5 had significant effects on caregiver distress, disruptions of family and social life contributed the most (β = 0.462), almost 3 times that of caregiving burden (β = 0.162). Positive emotional experiences also substantially reduced distress (β = -0.310). CONCLUSIONS AND IMPLICATIONS: Understanding the multifaceted nature of caregiver distress is crucial for developing effective strategies to support caregivers. In addition to reducing caregiving burden, having flexible resources and policies to minimize disruptions to caregivers' families (eg, flexible work policies; family-oriented education, training, and counseling) and enhance the positive aspects of caregiving may more effectively reduce distress.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".