Digital Documentation Platforms in Prehospital Care- Do They Support the Nursing Care
Bibliographic record
Abstract
This study examines and describe the ambulance nurse's experience of nursing documentation in single responder and the transfer of the documentation to other care levels. A qualitative design was used with focus group interviews as data collection method to enhance knowledge of the everyday experience of nursing documentation. The ambulance service in Sweden is a profession in transition that evolved from being a transport organization to provide advanced medical care and nursing. However, all patients do not need advanced medical treatment and the Single responder is an alternative resource to the ambulance that is used when no life-threatening conditions exists. However, the nurse faces a number of challenges when documenting nursing care interventions related to technological development and the mismatch between the care offered and people's demands and needs. Even though nursing care documentation is key to enhance and develop patient safety within a young field as ambulance service. There is a lack of a coherent documentation system and two themes emerged through content analyzes which conveyed how nursing care becomes invisible and how nursing care interventions are communicated through a hidden language. There are serious shortcomings in the transfer of nursing documentation to other care levels as well as deficiencies in the nursing documentation. Which jeopardizes the quality of care and patient safety as well as a systematic development of nursing care in this field.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".