Is it better to seek or to receive? A dual-factor model of social support
Bibliographic record
Abstract
Objectives: This study adopts a dual-factor approach to examine the association of seeking and receiving social support with 6 indicators of current functioning and 14 psychosocial strengths. Methods: A survey completed by 440 youth ages 10 to 21 (M = 16.38, SD = 3.04) assessed strengths, functioning, and victimization. Youth were classified into four groups: Interconnected (high on social support seeking and receiving; 33% of sample), Rebuffed (high on social support seeking, low on social support receiving; 12%), Tended (low on social support seeking, high on social support receiving; 16%), and Isolated (low on social support seeking and receiving; 39%). Results: Controlling for age, gender, and victimization, the social support group was associated with each meaning making, regulatory, and interpersonal strength, and every indicator of current functioning except trauma symptoms. The Isolated group scored lowest on all measures and the Interconnected group scored highest on 19 of 20 measures. The mixed profile groups fell between these extremes. Notably, the Rebuffed group reported higher levels of some strengths and non-theistic spiritual well-being than the Tended group. The Tended group was never significantly higher than the Rebuffed group. Implications: Individual skills and attitudes regarding helpseeking may be more impactful than social support provided by others. Rebuffed youth may be steeling themselves in other strengths when the social environment is not supportive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".