Patient experiences of uncertainty - a synthesis to guide nursing practice and research
Bibliographic record
Abstract
AIM: The aim of this study was to provide a synthesis of patients' experiences of uncertainty in illness and the interventions outlined, based on qualitative research. BACKGROUND: There is a need to explore various patient experiences from a nursing perspective in order to achieve evidence-based practice and improve the quality of care. Uncertainty in illness is a dynamic experience - a stressor with a major impact on patients' illnesses. METHODS: A literature search performed on PubMed and Cinahl yielded 15 qualitative studies that met the inclusion criteria and which were analysed and interpreted. RESULTS: Experienced uncertainty was one of two main areas comprising three themes: explaining, feeling and facing uncertainty. The second main area was suggested intervention strategies consisting of three themes: organizing the patient trajectory throughout the health-care system, supporting patients through relationships and providing knowledge through clear and accurate communication. CONCLUSION: Providing insight, confidence and supporting the patients' feeling of control are of importance for health-care professionals. IMPLICATIONS FOR NURSING MANAGEMENT: Structured organization of the trajectory system should be followed up, while outcome measures (patient satisfaction), education and training programmes for patients and families after discharge to improve coping strategies and reduce uncertainty should be developed. Nurse leaders should work towards the establishment of clinical academic nursing positions to integrate knowledge, skills, experiences and research into everyday routines.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".