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Record W2124740164 · doi:10.1177/1541931213571243

Improving Perceived Fairness of Task Assignments in Cardiovascular Intensive Nursing Care Unit with Simple Queuing Mechanism

2013· article· en· W2124740164 on OpenAlexafffund
Mehdi Saffarian, Wayne C.W. Giang

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceScheduling (production processes)Task (project management)Queueing theoryWorkflowNursingIntensive care unitNeonatal intensive care unitDeadline-monotonic schedulingPsychologyDynamic priority schedulingOperations managementMedicineComputer networkRound-robin schedulingEngineering

Abstract

fetched live from OpenAlex

In this work, a simple computer application is proposed to improve the perceived fairness of Cardiovascular Intensive Care Unit (CVICU) nursing team in hospitals. The proposed system called Intensive care Unit (ICU) Task Assignment Platform (ICU-TAP) is aimed to increase perceived fairness and satisfaction through a systematic but flexible scheduling method. The solution proposed here takes into account the skill preference of each nurse to ensure an equal opportunity to maintain career goals, with an aim to increasing job satisfaction. The system is designed based on the basic concept of queuing. ICU-TAP equally distributes the harder and monotonic tasks among nurses and takes into account the desired skill of each nurse for more frequent assignment of other tasks. The proposed system is expected to make the workflow of the unit traceable, resulting in paperless scheduling, and reducing discrepancies between scheduling and daily task assignment of the nursing unit.

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.

How this classification was reachedexpand

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.302
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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