Gender Inequalities of Health and Quality of Life in Informal Caregivers in Spain: Protocol for the Longitudinal and Multicenter CUIDAR-SE Study
Bibliographic record
Abstract
BACKGROUND: The aging population and increased disability prevalence in Spain have heightened the demand for long-term care. Informal caregiving, primarily performed by women, plays a crucial role in this scenario. This protocol outlines the CUIDAR-SE study, focusing on the gender-specific impact of informal caregiving on health and quality of life among caregivers in Andalusia and the Basque Country from 2013 to 2024. OBJECTIVE: This study aims to analyze the gender differences in health and quality of life indicators of informal caregivers residing in 2 Spanish autonomous communities (Granada, Andalusia, and Gipuzkoa; Basque Country) and their evolution over time, in relation to the characteristics of caregivers, the caregiving situation, and support received. METHODS: The CUIDAR-SE study uses a longitudinal, multicenter design across 3 phases, tracking health and quality of life indicators among informal caregivers. Using a questionnaire adapted to the Spanish context that uses validated scales and multilevel analysis, the research captures changes in caregivers' experiences amid societal crises, notably the 2008 economic crisis and the COVID-19 pandemic. A multistage randomized cluster sampling technique is used to minimize study design effects. RESULTS: Funding for the CUIDAR-SE study was in 3 phases starting in January 2013, 2017, and 2021, spanning a 10-year period. Data collection commenced in 2013 and continued annually, except for 2016 and 2020 due to financial and pandemic-related challenges. As of March 2024, a total of 1294 participants have been enrolled, with data collection ongoing for 2023. Initial data analysis focused on gender disparities in caregiver health, quality of life, burden, perceived needs, and received support, with results from phase I published. Currently, analysis is ongoing for phases II and III, as well as longitudinal analysis across all phases. CONCLUSIONS: This protocol aims to provide comprehensive insights into caregiving dynamics and caregivers' experiences over time, as well as understand the role of caregiving on gender inequality in health, considering regional variations. Despite limitations in participant recruitment, focusing on registered caregivers, the study offers a detailed exploration of the health impacts of caregiving in Spain. The incorporation of a gender perspective and the examination of diverse contextual factors enrich the study's depth, contributing significantly to the discourse on caregiving health complexities in Spain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58440.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".