Patient and stakeholder involvement in resilient healthcare: an interactive research study protocol
Bibliographic record
Abstract
INTRODUCTION: Resilience in healthcare (RiH) is understood as the capacity of the healthcare system to adapt to challenges and changes at different system levels, to maintain high-quality care. Adaptive capacity is founded in the knowledge, skills and experiences of the people in the system, including patients, family or next of kin, healthcare providers, managers and regulators. In order to learn from and support useful adaptations, research is needed to better understand adaptive capacity and the nature and context of adaptations. This includes research on the actors involved in creating resilient healthcare, and how and in what circumstances different groups of patients and other key healthcare stakeholders enact adaptations that contribute to resilience across all levels of the healthcare system. METHODS AND ANALYSIS: This 5-year study applies an interactive design in a two-phased approach to explore and conceptualise patient and stakeholder involvement in resilient healthcare. Study phase 1 is exploratory and will use such data collection methods as literature review, document analysis, interviews and focus groups. Study phase 2 will use a participatory design approach to develop, test and evaluate a conceptual model for patient and stakeholder involvement in RiH. The study will involve patients and other key stakeholders as active participants throughout the research process. ETHICS AND DISSEMINATION: The RiH research programme of which this study is a part is approved by the Norwegian Centre for Research Data (No. 864334). Findings will be disseminated through scientific articles, presentations at national and international conferences, through social media and popular press, and by direct engagement with the public, including patient and stakeholder representatives.
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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 | 0.140 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".