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Record W2167715098 · doi:10.5430/jnep.v1n1p25

Computerized documentation and community health nursing students

2011· article· en· W2167715098 on OpenAlexvenueno aff
Nadine M. Aktan, Janet Tracy, Connie Gleim Bareford

Bibliographic record

VenueJournal of Nursing Education and Practice · 2011
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationNursingNursing processNurse educationHealth careMedical educationNursing documentationMedicineNursing researchNursing carePsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Background/Objective: Proficiency in computerized documentation systems is an essential element of most areas of nursing practice today. Community health is one example of an area of nursing practice where computerized documentation systems help in the provision of high quality care. Nursing students must learn the basic principles of and begin to participate in the practices of computerized nursing documentation. It is, therefore, the responsibility of nursing faculty to promote student involvement in this important process. Methods: Two different faculty experiences with students participating in computerized nursing documentation were described using different electronic systems, a notebook computer system and a Personal Digital Assistant (PDA) system. Results/Conclusions: After reviewing the results of this descriptive experience, it is recommended that before students participate in computerized documentation, they receive written instructions. Sample charts, practice under direct faculty and staff guidance, and standardizing the learning experience are imperative. Educating the student in a technological environment is no longer optional for nurse faculty as the accurate documentation, transmission and management of data assures that the best practices are maintained, the proper billing of visits can be ensured, and the communication between the nursing student and community health nurses, as well as all members of the multi-disciplinary team is fostered.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.119
GPT teacher head0.494
Teacher spread0.375 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations2
Published2011
Admission routes1
Has abstractyes

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