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Record W4390957078 · doi:10.5334/ijic.icic23538

The contribution of the charge nurse in integrated healthcare

2023· article· en· W4390957078 on OpenAlexaff
Maripier Jubinville

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsHealth careInterpersonal communicationNursingTeamworkPsychologyMEDLINEContext (archaeology)Medical educationMedicineKnowledge managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Background: A charge nurse (CN) is defined as a nurse who holds a clinical-administrative management position. The role requires five essential skills: leadership; interpersonal communication; clinical-administrative caring; problem solving; and knowledge and understanding of the work environment. According to Plourde (2012), these skills can be divided into sub-skills that are needed in practice by CNs when upholding appropriate standards of quality of care in healthcare delivery. These skills are also essential for CNs when facilitating the process of integrated care within healthcare facilities. However, the scientific literature describes less-than-ideal work performance by CNs that relates to a lack of specialized and adapted training. Necessary skills are poorly addressed in the education of CNs, and they receive little formal support upon entry to practice. CNs play an essential role in coordinating processes surrounding integrated care in healthcare facilities, however the literature is scarce on this role of the CN. Objective: To demonstrate the extent to which the CN, by fulfilling their role, contributes to integrated care within healthcare facilities. Methods: A scoping review using the Joanna Briggs Institute (2020) framework was performed. Using a prepared search strategy, CINAHL, MEDLINE, Cairn, and grey literature databases were queried for articles written in French or English and published between 2000 and 2022. Articles mentioning at least one of the five CN skills named by Plourde (2012) were selected. Articles were analyzed according to “Leviers et obstacles au développement de l’ISS” by Longpré (2017) (Facilitators and barriers to the development of the integrated care model) which describes essential elements to consider in order to foster integrated healthcare. Results: 150 articles were retained and analyzed. These articles describe CN skills and how they are operationalized in practice. It was found that the skills and sub-skills of the CN are often addressed in articles describing integrated care, including for example the coordination of a patient's care during their hospitalisation. In addition, CNs allow for accessibility, continuity and integration of care. As such, CNs are key player in the implementation and development of better integrated care. Conclusion: This study illuminates the relevance of providing essential CN training that reinforces the CN skill set which in turn improves the quality and safety of integrated care in healthcare facilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.431
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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