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Record W2346598979 · doi:10.1145/2851581.2892473

Understanding Nurses' Perception Regarding the Use of NFC Application During Medication Administration

2016· article· en· W2346598979 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsUsabilityNear field communicationPerceptionProcess (computing)Health careComputer scienceAdministration (probate law)Sample (material)Human–computer interactionPsychologyTelecommunications

Abstract

fetched live from OpenAlex

Near Field Communication is an effective short-range wireless technology that has been used to securely identify objects. While many healthcare domains have benefited from the use of NFC, it currently is in limited use in medication administration in hospitals. The long-term objectives of this research are to ease and enhance the process of medication administration through designing an NFC-based framework for checking the five rights of medication administration. Before we can design such a framework, we must first understand the nurses' perception regarding the use of the NFC technology during the medication administration process. Second, we must gather recommendations that can be used as guidelines to design the framework. Therefore, in this paper we present a usability testing of a small sample of the framework to help reach these objectives.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.125
GPT teacher head0.285
Teacher spread0.160 · 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

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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