Loss of productivity among caregivers of dependent family members
Bibliographic record
Abstract
Background: Assuming the care of a dependent family member can constitute a health risk factor that significantly reduces the productivity of family caregivers, compromising their future and quality of life. Objective: Our purpose of this study was to investigate the productivity activities that are altered in the caregivers of a dependent family member and the impact this has on their satisfaction and quality of life. Methods: An analytical observational study was conducted for 500 caregivers of dependent family members. Sociodemographic data were collected for caregivers and their dependent family members. Carers' occupational performance and satisfaction were assessed using the Canadian Occupational Performance Measure (COPM), and quality of life was assessed using the World Health Organization - Quality of Life (WHOQOL-Bref). Comparison between groups was performed using the Chi-square test for qualitative variables. Pearson's correlation coefficient was used to assess the strength and direction of linear associations between numerical variables. Results: Most caregivers were women (72.3%, n = 364), with an average age of 60.3 ± 13.64 years. These individuals were providing care for a family member with severe dependency (69.7%, n = 348), classified as Grade III. Up to 38.2% (n = 191) of caregivers struggled with maintaining punctuality and consistency in their paid employment, and 25.6% (n = 128) of caregivers stopped or had difficulty working. In addition, 28.4% (n = 142) of caregivers had problems with household cleaning and tidying, 20.4% (n = 102) perceived that they neglected their other family members, and 18.6% (n = 93) of caregivers encountered problems attending courses and furthering their education. Women were more affected in terms of productivity. Moreover, performance and quality of life worsened as the number of productivity activities affected increased. Conclusions: Caring for a dependent family member has a considerable impact on the caregiver's productive activities, affecting their work performance, household management, and professional development, with a particularly marked impact on women. This caregiving role is also associated with a decrease in quality of life, which highlights the need for interventions to support caregivers in these areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".