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Record W2800704373 · doi:10.1055/s-0038-1642609

Nursing Information Flow in Long-Term Care Facilities

2018· article· en· W2800704373 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueApplied Clinical Informatics · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of VictoriaAlberta Health Services
FundersAlberta Health Services
KeywordsLong-term careNursingNursing careInformation systemMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Long-term care (LTC), residential care requiring 24-hour nursing services, plays an important role in the health care service delivery system. The purpose of this study was to identify the needed clinical information and information flow to support LTC Registered Nurses (RNs) in care collaboration and clinical decision making. METHODS: This descriptive qualitative study combines direct observations and semistructured interviews, conducted at Alberta's LTC facilities between May 2014 and August 2015. The constant comparative method (CCM) of joint coding was used for data analysis. RESULTS: Nine RNs from six LTC facilities participated in the study. The RN practice environment includes two essential RN information management aspects: information resources and information spaces. Ten commonly used information resources by RNs included: (1) RN-personal notes; (2) facility-specific templates/forms; (3) nursing processes/tasks; (4) paper-based resident profile; (5) daily care plans; (6) RN-notebooks; (7) medication administration records (MARs); (8) reporting software application (RAI-MDS); (9) people (care providers); and (10) references (i.e., books). Nurses used a combination of shared information spaces, such as the Nurses Station or RN-notebook, and personal information spaces, such as personal notebooks or "sticky" notes. Four essential RN information management functions were identified: collection, classification, storage, and distribution. Six sets of information were necessary to perform RN care tasks and communication, including: (1) admission, discharge, and transfer (ADT); (2) assessment; (3) care plan; (4) intervention (with two subsets: medication and care procedure); (5) report; and (6) reference. Based on the RN information management system requirements, a graphic information flow model was constructed. CONCLUSION: This baseline study identified key components of a current LTC nursing information management system. The information flow model may assist health information technology (HIT) developers to consolidate the design of HIT solutions for LTC, and serve as a communication tool between nurses and information technology (IT) staff to refine requirements and support further LTC HIT research.

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 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: Empirical
Teacher disagreement score0.915
Threshold uncertainty score1.000

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)0.0000.001

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 head0.029
GPT teacher head0.373
Teacher spread0.344 · 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