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Record W2120537531 · doi:10.12927/cjnl.2008.20285

A Case Study: The Initiative to Improve RN Scheduling at Hamilton Health Sciences

2008· article· en· W2120537531 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueNursing leadership · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsNurse scheduling problemScheduling (production processes)Health careNursingHuman resourcesBusinessFlexible schedulingOperations managementMedicinePsychologyManagementPolitical scienceEngineeringTwo-level schedulingDynamic priority schedulingScheduleEconomics

Abstract

fetched live from OpenAlex

In 2003, Hamilton Health Sciences embarked on an initiative to improve and standardize nursing schedules and scheduling practices. The scheduling project was one of several initiatives undertaken by a corporate-wide Nursing Resource Group established to enhance the work environment and patient care and to ensure appropriate utilization of nursing resources across the organization's five hospitals. This article focuses on major activities undertaken in the scheduling initiative. The step-by-step approach described, plus examples of the scheduling resources developed and samples of extended-tour schedules, will all provide insight, potential strategies and practical help for nursing administrators, human resources (HR) personnel and others interested in improving nurse scheduling.

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)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.710
GPT teacher head0.468
Teacher spread0.242 · 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