Strengthening resilience and mental wellbeing through the Support4Resilience Toolbox for leaders in elderly care: A protocol for a cross-country mixed methods study (Support4Resilience) (Preprint)
Bibliographic record
Abstract
BACKGROUND: Older adult care systems face severe workforce shortages, rising demands, and high levels of stress and burnout, undermining the quality of care and organizational resilience. Support4Resilience (S4R, 2024-2028) aims to improve working conditions and mental well-being by equipping leaders with an evidence-based, organizational-level intervention. The project develops and evaluates a digital S4R Toolbox consisting of 3 tools: (1) mapping and identification (MAP); (2) reflection and education (IMPROVE); and (3) reorganization (REMOVE). OBJECTIVE: The project aims to strengthen resilience and mental well-being among health care workers and informal caregivers in older adult care across Europe and Australia through the development and implementation of the digital S4R Toolbox. Secondary objectives are identifying determinants of resilience and mental well-being across diverse contexts; exploring needs and perspectives that inform successful adaptation to changing working conditions and ethical challenges; designing the S4R Toolbox; evaluating its relevance, effectiveness, and cost-effectiveness across health care systems; advancing theory on the relationship among individual resilience, organizational resilience, and leadership; and producing research-based recommendations and interventions through the open-access S4R Resource Bank. METHODS: S4R applies an exploratory, longitudinal, mixed-methods co-design approach across 4 phases. The input phase gathers evidence through literature reviews, context mapping, and qualitative and quantitative data collection in 7 countries. The co-design and prototype testing phase involves developing the S4R Toolbox and conducting pilot testing. The implementation, evaluation, and finalization phase includes a 1-year implementation period, followed by process, effectiveness, and cost-effectiveness evaluations and final refinement of the Toolbox. The output phase disseminates the results through the open-access S4R Resource Bank. RESULTS: The project has achieved substantial early progress, including 5 literature reviews, completed and published context mapping, and comprehensive data collection involving health care workers, leaders, and informal caregivers in 7 countries. Toolbox development is well advanced, and pilot testing has been completed. CONCLUSIONS: S4R will deliver a research-based digital Toolbox that supports leaders in strengthening the resilience and mental well-being of health care workers and informal caregivers in older adult care. By integrating the perspectives and experiences of leaders, health care workers, and informal caregivers, identifying resilience factors, and developing theory-informed, cost-effective interventions, S4R will provide actionable resources through an open-access platform, contributing to more resilient older adult care systems. TRIAL REGISTRATION: ClinicalTrials.gov NCT07504042; https://clinicaltrials.gov/ct2/show/NCT07504042. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73701.
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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 | 0.045 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.103 | 0.016 |
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".