Family Social Capital’s Impact on the Family Stress Model: A Cross-Sectional Spanish Study During the COVID-19 Pandemic
Bibliographic record
Abstract
This article has a dual purpose. First, it corroborates the empirical evidence of the Family Stress Model (FSM) to the Spanish population during the COVID-19 pandemic. Second, this article contributes to extending the FSM by investigating the impact of Family Social Capital (FSC) on the well-established FSM. In other words, it assesses if families with high FSC, when affected by economic hardship and distress, avoid the deterioration of their inner relationships. We conducted empirical analyses through data collected from N = 583 (mean age = 49.5 years) from Spanish families with diverse working situations using the Structural Equation Modelling (SEM) technique to test the theorized model. Our results suggest the validity of the FSM in the context of the economic crisis due to COVID-19 in Spain and then, by complementing the FSM with FSC, demonstrate that FSC can be considered as an accommodating protective factor for sustaining couple and children relationship quality for families facing economic difficulties. Also, in this article, the positive effects of FSC are studied within families in an original way, unlike past studies, which mainly used the FSC to relate civic social capital and showed its beneficial effects in extra-familial areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".