ArticleJournal of healthcare engineering2022
Research on a Health Care Personnel Training Model Based on Multilayered Knowledge Mapping for the Integration of Nursing Courses and Examinations.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Retracted: Research on a Health Care Personnel Training Model Based on Multilayered Knowledge Mapping for the Integration of Nursing Courses and Examinations.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-09-20Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/095381800BD8389F56B58F4BC76DB9
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While nursing courses provide a convenient and quick way to learn, they can also be overloaded with resources that can cause learners to become cognitively disoriented or have difficulty choosing nursing course. This paper proposes to fully explore learners' interests in the case of sparse data by fusing knowledge graph technology and deep recommendation models and adopt knowledge graph to model nursing courses at the semantic level so as to correspond the set of nursing courses to the knowledge graph and solve the problem of lack of logical knowledge relationships. Due to the specificity of its positions, the nursing profession must accurately position the nursing professional curriculum standards in the process of determining the talent cultivation model based on the nursing professional positions and the admission requirements for nursing practice qualification. Through linear feature mining based on the knowledge graph, entities and relationships are used to intuitively display the interest paths of nursing professional learners and enhance the interpretability of recommendations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.