Developing the Resilience Framework for Nursing and Healthcare
Bibliographic record
Abstract
Despite four decades of resilience research, resilience remains a poor fit for practice as a scientific construct. Using the literature, we explored the concepts attributed to the development of resilience, identifying those that mitigate symptoms of distress caused by adversity and facilitate coping in seven classes of illness: transplants, cancer, mental illness, episodic illness, chronic and painful illness, unexpected events, and illness within a dyadic relationship. We identified protective, compensatory, and challenge-related coping-concept strategies that healthcare workers and patients use during the adversity experience. Healthcare-worker assessment and selection of appropriate coping concepts enable the individual to control their distress, resulting in attainment of equanimity and the state of resilience, permitting the resilient individual to work toward recovery, recalibration, and readjustment. We inductively developed and linked these conceptual components into a dynamic framework, The Resilience Framework for Nursing and Healthcare, making it widely applicable for healthcare across a variety of patients.
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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.011 |
| 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".