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Record W1945710635 · doi:10.12968/bjon.2015.24.9.484

Managing pain medications in long-term care: nurses' views

2015· article· en· W1945710635 on OpenAlexafffundabout
Sharon Kaasalainen, Gina Agarwal, Lisa Dolovich, Kevin Brazil, Αλεξάνδρα Παπαϊωάννου

Bibliographic record

VenueBritish Journal of Nursing · 2015
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsMcMaster UniversityHealth Sciences Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineNursingNarcoticLong-term careAddictionFamily medicineContinuing educationPsychiatryMedical education

Abstract

fetched live from OpenAlex

The purpose of this study was to explore nurses' perceptions of their current practices related to administering pain medications to long-term care (LTC) residents. A cross-sectional survey design was used, including both quantitative and open-ended questions. Data were collected from 165 nurses (59% response rate) at nine LTC homes in southern Ontario, Canada. The majority (85%) felt that the medication administration system was adequate to help them manage residents' pain and 98% felt comfortable administering narcotics. In deciding to administer a narcotic, nurses were influenced by pain assessments, physician orders, diagnosis, past history, effectiveness of non-narcotics and fear of making dosage miscalculations or developing addictions. Finally, most nurses stated that they trusted the physicians and pharmacists to ensure orders were safe. These findings highlight nurses' perceptions of managing pain medications in LTC and related areas where continuing education initiatives for nurses are needed.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.341
Teacher spread0.304 · 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
Published2015
Admission routes3
Has abstractyes

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