MétaCan
Menu
Back to cohort
Record W3208658681 · doi:10.5737/23688076314476482

Patient needs and resource intensity weighting in the ambulatory care unit

2021· article· en· W3208658681 on OpenAlexaffvenue
Andrea Knox, John Larmet

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsKelowna General Hospital
Fundersnot available
KeywordsStaffingOperationalizationSpecialtyAmbulatory careNursingMedicineUnit (ring theory)Resource (disambiguation)Best practiceWork (physics)Scope of practiceScope (computer science)AmbulatoryHealth carePsychologyFamily medicineComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

Across British Columbia Cancer (BC Cancer), oncology nurses work as part of an interdisciplinary team in the outpatient ambulatory care unit (ACU) and support patients across the trajectory of their cancer journey. Previous initiatives, which focused on identifying patient needs and nursing role optimization work, have enhanced role clarity, enabling nurses to articulate their scope of practice and specialty competencies required to best meet the needs of patients and families. However, while the patient needs and fundamental practice elements have been identified to optimize the ACU nursing role, a gap still exists in quantifying the staffing resources required to operationalize the current model of care. To address this gap, a quality improvement project was initiated to develop an internally validated ACU Nursing Resource Intensity Weighting (RIW) tool for projecting baseline staffing requirements. The tool can be utilized to inform strategic and operational planning discussions focused on improving the outpatient model of care in oncology.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.245
Teacher spread0.216 · 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 designNot applicable
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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Oncology Nursing JournalSame topicEconomic and Financial Impacts of CancerFrench-language works237,207