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Record W2228726167 · doi:10.1177/1084822315607231

Knowledge Tools for Cancer Treatment–Related Symptom Management by Home Care Nurses

2015· article· en· W2228726167 on OpenAlexaff
Kathryn Nichol, Dawn Stacey, Craig Kuziemsky, Wendy Gifford, Shelley Mackenzie

Bibliographic record

VenueHome Health Care Management & Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUsabilityReadabilityWorkflowThematic analysisDocumentationDescriptive statisticsNursingMedicineKnowledge managementMedical educationComputer scienceQualitative researchHuman–computer interaction

Abstract

fetched live from OpenAlex

A mixed methods descriptive study explored 38 nurses’ perceptions of usability and factors influencing the use of 13 symptom management protocols when caring for patients with cancer in the home. Descriptive analysis of the usability survey revealed protocols had high readability, right amount of information, appropriate terms, and were self-evident to complete. Thematic analysis of transcripts identified barriers to using the protocols including lack of time, paper-based documentation systems, and length of the protocols. Facilitating factors included high usability of the protocols, integration of a remote symptom support nursing role, and the usefulness for education and training. In summary, the protocols were described as a useful resource and beneficial for nursing orientation but required support to overcome some barriers and improve fit with clinical workflow.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.119
GPT teacher head0.481
Teacher spread0.362 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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