The relationships between resilience, care environment, and social-psychological factors in orphaned and separated adolescents in Western Kenya
Bibliographic record
Abstract
The relationships between care environment, resilience, and social factors in orphaned and separated adolescents and youths (OSAY) in western Kenya are complex and under-studied. This study examines these relationships through the analysis of survey responses from OSAY living in Charitable Children's Institutes (CCI) and family-based care settings (FBS) in Uasin Gishu County, Kenya. The associations between 1) care environment and resilience (measured using the 14-item Resilience Scale); 2) care environment and factors thought to promote resilience (e.g. social, family, and peer support); and 3) resilience and these same resilience-promoting factors, were examined using multivariable linear and logistic regressions. This cross-sectional study included 1202 OSAY (50.4% female) aged 10-26 (mean=16; SD=3.5). The mean resilience score in CCIs was 71 (95%CI=69-73) vs. 64 (95%CI=62-66) in FBS. OSAY in CCIs had higher resilience (β=7.67; 95%CI=5.26-10.09), social support (β=0.26; 95%CI=0.14-0.37), and peer support (β=0.90; 95%CI=0.64-1.17) than those in FBS. OSAY in CCIs were more likely to volunteer than those in FBS (OR=3.72; 95%CI=1.80-7.68), except in the male subgroup. Family (β=0.42; 95%CI=0.24-0.60), social (β=4.19; 95%CI=2.53-5.85), and peer (β=2.13; 95%CI=1.44-2.83) relationships were positively associated with resilience in all analyses. Volunteering was positively associated with resilience (β=5.85; 95%CI=1.51-10.19). The factor most strongly related to resilience in both fully adjusted models was peer support. This study found a strong relationship between care environment and resilience. Care environment and resilience each independently demonstrated strong relationships with peer support, social support, and participating in volunteer activities. Resilience also had a strong relationship with familial support. These data suggest that resilience can be developed through strategic supports to this vulnerable population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".