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Record W2508914406

Assessing the Impact of Nursing Informatics Competencies on Decision Making Satisfaction: Results of a Preliminary Study

2016· article· en· W2508914406 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

VenueAmericas Conference on Information Systems · 2016
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsMcMaster UniversityLakehead University
Fundersnot available
KeywordsInformaticsHealth informaticsHealth careNursingKnowledge managementMedicineComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Nursing informatics competencies (NIC) refer to the knowledge and skills needed to effectively use technological resources to provide effective care using health care technology (TIGER, 2006). NICs are crucial in providing patient-centered care in healthcare, an industry that uses different technology to support clinical activities, for example, electronic health records (EHR). This study will provide an overview of the benefits of NICs, and propose and validate a theoretical model that can be used to assess the impact of nurses NICs on their satisfaction with their decisions resulting in the use of decision support systems. This preliminary research is important to medical administration and management as well as medical educators because it will demonstrate how this research should advance as it will determine if decision making satisfaction is a viable outcome of nurses and their level of NIC.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.722
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.153
GPT teacher head0.506
Teacher spread0.353 · 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