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Record W3049282768 · doi:10.14687/jhs.v17i3.5889

Comparison of pain assessments made by patients and nurses in emergency services

2020· article· en· W3049282768 on OpenAlexaboutno aff
Nefise Cevriye Sucu Çakmak, Nurcan Çalışkan

Bibliographic record

VenueJournal of Human Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDescriptive statisticsWilcoxon signed-rank testTest (biology)Family medicineNursingPhysical therapyMann–Whitney U test

Abstract

fetched live from OpenAlex

Aim: This research aimed to compare pain assessments made by patients and by nurses. Method: This descriptive and comparative research was conducted in a state, a university and a private hospital emergency service monitoring unit that had the highest number of patient admissions in Ankara, Turkey. The research sample consisted of 175 patients and 35 nurses. For the collection of data, the following sources were consulted: the patient information form, the nurse information form and the McGill Pain Questionnaire. For evaluation of the data, number and percentage calculations, chi-square analysis, kappa compliance analysis and the Wilcoxon sign test were used. Written consent was received from the ethical council and hospitals as well as from nurses and patients. Results: 74.3% of nurses stated that they do not think that the pain level expressed by the patient is always right. While the point average given by patients related to all dimensions define the characteristics of pain was 20.48 ± 10.10, the average given by nurses was 14.35 ± 8.46. There is a statistically significant difference between the point averages given by patients and nurses related to total dimensions of pain characteristics (p<0.05). Conclusion: As a result, pain evaluations of nurses were found to be significantly lower than those of patients. It is suggested that the policies and procedures used in health personnel education institutes and health institutes related to pain management should be improved.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.412
Teacher spread0.367 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes1
Has abstractyes

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