MétaCan
Menu
Back to cohort
Record W2302077225 · doi:10.1188/16.cjon.98-101

Development of a Workshop for Malignant Hematology Nursing Education

2016· article· en· W2302077225 on OpenAlexaff
Karelin Martina, Lucia Ghadimi, Diana Incekol

Bibliographic record

VenueClinical journal of oncology nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineHematologyInternal medicineMultiple myelomaLeukemiaOncologyLymphomaCancerPopulationEnvironmental health

Abstract

fetched live from OpenAlex

As part of a comprehensive orientation for nurses caring for patients with hematologic malignancies, nurses are expected to attend general corporate orientation immediately followed by hospital site-specific nursing orientation. The orientation is comprised of lectures, e-learning, and clinical observership, as well as clinical practice under supervision of a preceptor. Nurses also are expected to attend foundational courses. The goal of these courses is to consolidate practical and theoretical knowledge in a specific oncology nursing specialty. A workshop was developed that offers a unique vision by interweaving theory, practice, and patient voice. .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0040.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0160.006

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 head0.138
GPT teacher head0.515
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueClinical journal of oncology nursingSame topicNeutropenia and Cancer InfectionsFrench-language works237,207