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Record W2472970386 · doi:10.1097/cin.0000000000000260

Nursing Information Systems Requirements

2016· article· en· W2472970386 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

VenueCIN Computers Informatics Nursing · 2016
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsNursingMedicine

Abstract

fetched live from OpenAlex

Considering the integral role of understanding users' requirements in information system success, this research aimed to determine functional requirements of nursing information systems through a national survey. Delphi technique method was applied to conduct this study through three phases: focus group method modified Delphi technique and classic Delphi technique. A cross-sectional study was conducted to evaluate the proposed requirements within 15 general hospitals in Iran. Forty-three of 76 approved requirements were clinical, and 33 were administrative ones. Nurses' mean agreements for clinical requirements were higher than those of administrative requirements; minimum and maximum means of clinical requirements were 3.3 and 3.88, respectively. Minimum and maximum means of administrative requirements were 3.1 and 3.47, respectively. Research findings indicated that those information system requirements that support nurses in doing tasks including direct care, medicine prescription, patient treatment management, and patient safety have been the target of special attention. As nurses' requirements deal directly with patient outcome and patient safety, nursing information systems requirements should not only address automation but also nurses' tasks and work processes based on work analysis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.064
GPT teacher head0.422
Teacher spread0.358 · 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