[Patients and caregivers experience on Nursing in Italy: scoping review].
Bibliographic record
Abstract
INTRODUCTION: Comparison of the state of nursing in Italy with other countries has shown that theory development in Italian nursing remains quite undeveloped. Theory development in Italian nursing will need to consider local cultural and professional aspects, specific to the Italian practice context, by drawing on known health needs, experiences and nursing approa- ches. The aim of this investigation was to map current knowledge related to nursing in Italy, based on the experiences of patients, families and communities, to provide a basis on which nursing theories could be developed. METHODS: Scoping Review was selected as the best method for this knowledge mapping. Fawcett's nursing metaparadigm was chosen as a broad guide and means by which the litera- ture analysis could be structured. RESULTS: Twenty-two studies were retained and examined in this analysis, including contexts relating to acute care, chronic conditions, as well as emergency and home care services. We defined themes in line with the nursing metaparadigm. Although these definitions are partial, referring only to certain contexts specific to some aspects of nursing care, the original contributions of this investigation provides an important starting point for theory development in Italian nursing, based on the Italian context. CONCLUSION: Strong and credible theory development, that can be readily adapted to practice, requires a rigorous analysis of the points of view of all actors involved in the nursing care process.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".