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Record W4288072734 · doi:10.1097/nhh.0000000000001005

Continuing Education for Home Care Nurses

2021· review· en· W4288072734 on OpenAlexaff
Michelle Pavloff, Mary Ellen Labrecque

Bibliographic record

VenueHome Healthcare Now · 2021
Typereview
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNursingCINAHLStaffingCompetence (human resources)MEDLINEMedicineBurnoutCredentialingContinuing educationNurse educationMedical educationPsychology

Abstract

fetched live from OpenAlex

With the wide range of clinical skills and responsibilities that home care nurses (HCNs) are expected to perform, it is important they are supported with access to relevant continuing nursing education (CNE) to perform their job safely and effectively. An integrative literature review was conducted to explore the current evidence on CNE for HCNs. Medline and CINAHL were searched and 13 articles that met the criteria were reviewed. The analysis identified three themes: (1) learning strategies (simulation, virtual gaming, elearning, traditional learning); (2) challenges (staffing, time, access, skill) and opportunities (incentive to stay employed, decreased burnout); and (3) learning needs (palliative, patient and family needs, older adults and dementia, acute nursing skills). Nurses who provide care to patients in their homes have very complex roles and responsibilities. In order to keep patients and nurses safe, standards of education for HCNs, beyond their basic education program, must be developed. These educational standards must be designed to address the complex medical needs of patients while making the educational opportunities accessible and value-added. Improving the CNE experience for HCNs has the potential to increase patient safety, improve care outcomes, increase nurse competence, improve retention, and decrease nurse burnout.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.164
GPT teacher head0.497
Teacher spread0.332 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2021
Admission routes1
Has abstractyes

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