Resilience following Child Maltreatment: A Review of Protective Factors
Bibliographic record
Abstract
OBJECTIVE: Child maltreatment is linked with numerous adverse outcomes that can continue throughout the lifespan. However, variability of impairment has been noted following child maltreatment, making it seem that some people are more resilient. Our review includes a brief discussion of how resilience is measured in child maltreatment research; a summary of the evidence for protective factors associated with resilience based on those studies of highest quality; a discussion of how knowledge of protective factors can be applied to promote resilience among people exposed to child maltreatment; and finally, directions for future research. METHOD: The databases MEDLINE and PsycINFO were searched for relevant citations up to July 2010 to identify key studies and evidence syntheses. RESULTS: Although comparability across studies is limited, family-level factors of stable family environment and supportive relationships appear to be consistently linked with resilience across studies. There was also evidence for some individual-level factors, such as personality traits, although proxies of intellect were not as strongly related to resilience following child maltreatment. CONCLUSIONS: Findings from resilience research needs to be applied to determine effective strategies and specific interventions to promote resilience and foster well-being among maltreated children.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".