A theoretical model of resilience capacity: Drawn from the words of adult children of alcoholics
Bibliographic record
Abstract
BACKGROUND: Resilience can be defined as exhibiting positive outcomes after serious threats to one's adaptation or development. AIMS: This study aimed to suggest a theoretical model of resilience capacity by validating and extending one of the existing nursing theories of resilience-the society-to-cell model developed by Szanton and Gill. To do so, we conducted a qualitative study exploring the factors and conditions influencing adaptation in children of alcoholics (i.e., people who grew up under alcoholic parents). METHODS: Data were collected from 22 adult children of alcoholics in South Korea via semistructured interviews. All interviews were audio-recorded and transcribed, and the data were analyzed using directed content analysis. RESULTS: The results revealed two categories each at the society and community levels, and three categories each at the family and individual levels. No categories emerged at the physiological and cellular levels. In summary, resilience capacity is determined by the multilevel (e.g., society, community, family, and individual levels) factors that all individuals possess. CONCLUSION: This study is meaningful in that it presents concrete goals for nurses to pursue-namely, enhancing individuals' resilience capacity-and suggests strong evidence for developing nursing intervention programs that can foster resilience capacity.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".