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Record W2132782558 · doi:10.1145/1240624.1240661

An observational study on information flow during nurses' shift change

2007· article· en· W2132782558 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
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsObservational studyDemographicsComputer scienceInformation flowInformation sharingVariety (cybernetics)MultitudeData scienceHuman–computer interactionWorld Wide WebMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

We present an observational study that was conducted to guide the design and development of technologies to support information flow during nurses' shift change in a hospital ward. Our goal is to find out how the complex information sharing processes during nurses' brief shift change unfold in a hospital setting. Our study shows the multitude of information media that nurses access during the parallel processes of information assembly and disassembly: digital, paper-based, displayed and verbal media. An initial analysis reveals how the common information spaces, where information media are positioned and accessible by all participants, are actively used and how they interact with the personal information spaces ephemerally constructed by the participants. Several types of information are consistently transposed from the common information spaces to the personal information space including: demographics, historical data, reminders and to-dos, alerts, prompts, scheduling and reporting information. Information types are often enhanced with a variety of visual cues to help nurses carry out their tasks.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.591
GPT teacher head0.502
Teacher spread0.089 · 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