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Experiences of Nephrology Nurses in Assessing and Managing Pain in Patients Receiving Maintenance Hemodialysis

2020· article· en· W3011602519 on OpenAlexaff
Frances Fothergill Bourbonnais, Kelley Tousignant

Bibliographic record

VenueNephrology Nursing Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineNephrologyHemodialysisThematic analysisContext (archaeology)Kidney diseaseIntensive care medicineChronic renal failureNursingQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a major health problem. The purpose of this qualitative study was to describe nephrology nurses' experiences in assessing and managing pain in patients who were receiving maintenance hemodialysis at outpatient units within a tertiary care institution. Semi-structured interviews were conducted with seven nurses, and a thematic analysis was used to analyze data. Themes emerged related to the complexity of pain assessment and management in these patients, who were often elderly. Nurses had to ascertain whether the pain was related to hemodialysis treatment, renal failure, or comorbidities. Nurses described managing pain within the context of the hemodialysis unit, and this required working as a team. Nurses also described the need for a palliative approach in patient care.

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)0.008
metaresearch head score (Gemma)0.019
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0050.006
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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