Building Resilience: The Conceptual Basis and Research Evidence for Resilience Training Programs
Bibliographic record
Abstract
The relationship between adverse experiences and later development has been explored by many researchers, leading to the conceptualization of resilience as a factor explaining the normal or optimal development of some individuals exposed to adversity. Today many different interventions exist aiming to improve the ability of individuals to respond to adversity. In this narrative literature review, we evaluate the literature surrounding resilience and resilience training, discussing the quality of the evidence supporting resilience training, theoretical and practical differences between types of training, and the impact of resilience and psychological training on outcome measures across a variety of settings. The results of our review show that the quality of the literature is mixed, resilience training is not well differentiated from other forms of training, and that the impact of psychological training on later functioning depends heavily on the type of outcome measured and the setting of the training. Further research must be conducted prior to the implementation of resilience training programs in order to assure their efficacy and effectiveness in proposed contexts.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".