Subjective well-being in informal caregivers during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The study of subjective well-being (SWB) is important as it is related to the reduction of morbidity and mortality, with the maintenance of functionality and autonomy in the elderly population. The impact of the formative intervention on the SWB of informal caregivers (ICGs) during the pandemic crisis of COVID-19 was analyzed. This study is a quasi-experimental single-group, longitudinal study with a sample of 31 ICGs and their dependents. A form was used for data collection, and data processing was performed using IBM SPSS (Statistical Package for the Social Sciences), using descriptive statistics and inferential statistics. Of the total sample, the majority were female (90.3%). The difference between the mean of positive affection and negative affection at Moment 1 (M1) was –0.0581 ± 0.71590 and 0.04645 ± 0.53326 at Moment 2 (M2). The mean rank ordering of the difference between the two types of affection differed significantly between M2 and M1 (Wilcoxon: p < 0.000), with that of M2 being higher than M1 (16.93 > 2.50). The formative intervention, within the scope of community nursing, had a significant impact on increasing the SWB of the ICG in this sample. This study may contribute to improving the SWB of ICG and their dependents.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".