What is Resilience?
Bibliographic record
Abstract
OBJECTIVE: While everyone-including front-line clinicians-should strive to prevent the maltreatment and other severe stresses experienced by many children and adults in everyday life, psychiatrists and other health professionals also need to consider how best to support, throughout the lifespan, those people affected by severe adversity. The first step in achieving this is a clear understanding of the definitions and concepts in the rapidly growing study of resilience. Our paper reviews the definitions of resilience and the range of factors understood as contributing to it, and considers some of the implications for clinical care and public health. METHOD: This narrative review took a major Canadian report published in 2006 as its starting point. The databases, MEDLINE and PsycINFO, were searched for new relevant citations from 2006 up to July 2010 to identify key papers considering the definitions of resilience and related concepts. RESULTS: Definitions have evolved over time but fundamentally resilience is understood as referring to positive adaptation, or the ability to maintain or regain mental health, despite experiencing adversity. The personal, biological, and environmental or systemic sources of resilience and their interaction are considered. An interactive model of resilience illustrates the factors that enhance or reduce homeostasis or resilience. CONCLUSIONS: The 2 key concepts for clinical and public health work are: the dynamic nature of resilience throughout the lifespan; and the interaction of resilience in different ways with major domains of life function, including intimate relationships and attachments.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".