MétaCan
Menu
Back to cohort
Record W4416538785 · doi:10.1188/25.cjon.e178-e184

Measuring Nurse Knowledge, Attitudes, and Skills in Phase 1 Clinical Trials

2025· article· en· W4416538785 on OpenAlexaff
Tahani Dweikat, Jeannine M. Brant

Bibliographic record

VenueClinical journal of oncology nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsBrantford Energy (Canada)
Fundersnot available
KeywordsClinical trialDrug trialDrugPhase (matter)Phases of clinical researchOncology nursingMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Phase 1 clinical trials are the initial phase in advancing cancer treatment and testing a new drug for the first time in humans. No studies have investigated combined nurse knowledge, attitudes, and skills when administering phase 1 drugs. OBJECTIVES: This study examined oncology nurses' knowledge, attitudes, and skills; communication; and workflows regarding safe administration of phase 1 drugs. METHODS: A team at a large cancer organization in the southwestern United States conducted an exploratory, cross-sectional survey. The sample included acute and ambulatory care nurses who administer investigational drugs and clinical research nurses who oversee clinical trials. FINDINGS: Nurses scored highest in knowing the goal of a phase 1 drug and lowest in knowing which phase of investigational drug they were administering. For attitudes, confidence in administering and perceived safety when administering were the lowest mean scores. More than half had administered a phase 1 drug in the past five years. More oncology nursing experience correlated with understanding the nurse's role and negatively correlated with fear. About half of the nurses identified challenges in clinical trials workflows.

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)0.139
metaresearch head score (Gemma)0.321
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1390.321
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.009
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 head0.735
GPT teacher head0.754
Teacher spread0.018 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueClinical journal of oncology nursingSame topicEthics in Clinical ResearchFrench-language works237,207