Multiple Measures of Family and Social Support as Predictors of Psychological Well-Being: An Additive Approach
Bibliographic record
Abstract
The purpose of this study was to (1) examine the direct relations of multiple sources of social support on psychological well-being and (2) to examine the utility of an additive model on these variables, in a sample of 251 participants from a Southwestern Georgia University. The sources of support included family environment, friendship, family and significant other support, father’s bonding and mother’s bonding. Measures of psychological well-being included the summed total of Ryff’s Scale of Psychological Well-Being (PWB), as well as self-confidence-an additional measure of psychological well-being. In addition to direct effects, it was hypothesized that having multiple, rather than fewer sources of support would be more beneficial to an individual. Hierarchical regression analyses were conducted to test the unique variability each variable added, as well as to determine whether the additive model predicted PWB above and beyond singular sources of support. Results revealed that the hypotheses predicting direct relationships between the social supports of interest and PWB were largely supported by the data with the exception of father bonding. Results for the additive model revealed mixed results, indicating that having numerous concurrent support lines are beneficial in certain cases. The importance of having multiple social supports from which one can rely, especially when dealing with stressors and crises are also expressed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".