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Record W2050892727 · doi:10.1177/1043454204272539

Outpatient Chemotherapy Administration: Decreasing Wait Times for Patients and Families

2004· article· en· W2050892727 on OpenAlexaff
Eleanor Hendershot, Cory Murphy, Sandra Doyle, Judy Van-Clieaf, Jane Lowry, Lisa Honeyford

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsLeukemia & Lymphoma Society of CanadaSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineProtocol (science)Tracking (education)Ambulatory careNegotiationAmbulatoryQuality (philosophy)NursingMedical emergencyHealth carePsychologySurgeryAlternative medicine

Abstract

fetched live from OpenAlex

Increasingly, there is a trend to deliver chemotherapy, where possible, in the outpatient ambulatory setting. In the few studies that have explored the setting of cancer care, long wait times are frequently linked to dissatisfaction. Several factors contribute to lengthy waiting times for patients and their families: long registration processes, lag times associated with obtaining laboratory results, time required for patient assessments and preparation of chemotherapeutic agents, adequacy of nursing resources, and physical space constraints in relation to patient volumes. With the goal of improving care delivery in the outpatient clinic, a fast-tracking system was established. Program planning included establishing patient eligibility criteria, protocol and treatment appropriateness, interdepartmental collaboration, development of a communication plan for families and staff, negotiation of physical space, and allocation of human resources. This was instituted by re-allocating existing resources and establishing an autonomous nurse-managed chemotherapy clinic. This fast-tracking program has enabled us to use our existing resources with greater efficiency and improve patient care from safety and quality-of-life perspectives for those included in the program.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.052
GPT teacher head0.444
Teacher spread0.391 · 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 designObservational
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

Citations60
Published2004
Admission routes1
Has abstractyes

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