Telemedicine as an Untapped Opportunity for Parkinson’s Nurses Training in Personalized Care Approaches
Bibliographic record
Abstract
(1) Background: Parkinson nurses (PN) take over important functions in the telemedical care of person's with Parkinson's disease (PwPs). This requires special competencies that have so far been largely unexplored. The aim of the article is to identify competencies of PN operating in a personalized care model. (2) Methods: This study employed a qualitative approach. Based on the competency framework for telenursing, PN were asked about their competencies using a qualitative online survey. (3) Results: The results show that PN need competencies on a personal and organizational level, as well as in the relationship with PwPs. PN have developed these skills through professional exchange, training, and personal experience. In addition, both hindering and beneficial factors for the development of competencies could be identified. (4) Conclusions: Competency development for telemedical care is complex and must be designed and promoted in a targeted manner.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".