Designing resilience research: Using multiple methods to investigate risk exposure, promotive and protective processes, and contextually relevant outcomes for children and youth
Bibliographic record
Abstract
BACKGROUND: Inconsistent, poorly designed research on resilience in the human sciences has contributed to epistemological and ontological ambiguity which has fuelled claims that resilience as a concept is poorly theorized. OBJECTIVE: Building on research with abused and neglected children around the world, the objective of this paper is to show that studies of resilience must account for: (a) risk exposure (of relevance in different contexts); (b) promotive and protective processes (internal and external resources associated with resilience across systems); and (c) desired outcomes (as privileged by stakeholders in different cultures and contexts). METHOD: By identifying common aspects of resilience research from a purposeful selection of studies (ones with weak and strong methodologies), this paper identifies three dimensions of well-designed studies of childhood resilience. RESULTS: Attention to all three dimensions enhances both the empirical validity (in the quantitative research paradigm) and phenomenological trustworthiness (in qualitative research) of resilience research with children and families. Challenges researching resilience can also be resolved by designing studies that account for all three dimensions. These challenges include the lack of systemic thinking to account for contextual factors and other external threats to child wellbeing, and the excessive generalization of findings. CONCLUSION: This three-part model for resilience research reflects the very best practices among resilience researchers and has the potential to address the definitional and methodological ambiguity that plague studies of resilience.
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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 | 0.133 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".