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Record W3040384870 · doi:10.5539/gjhs.v12n9p44

Pain Management Practices by Nurses: Application of the Self-Efficacy Theory

2020· article· en· W3040384870 on OpenAlexvenueno aff
Bashar I. Alzghoul, Nor Azimah Chew Abdullah

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Self-efficacyPain managementDistressHealth careNursingMedicinePsychologyClinical psychologyPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

Pain management is one of the most recurrent healthcare services provided by nurses. Based on the proposition of the self-efficacy theory, knowledge and attitudes can affect the nurses; confidence and their ability to manage the patients’ discomforts, which consequently affects their ability to apply appropriate pain management practices. The study examines the relationship between knowledge and attitudes towards distress management and the nurse’s individual capability to manage pain. The research is a transverse, correlational design study involving 266 registered nurses (n = 266). The nurses were requested to provide information on pain management via three instruments: attitude to, knowledge of and self-efficacy of pain management. Statistically, the nurses displayed an essential relationship between self-efficacy and attitude towards pain management (β = 0.502, t = 10.119, p< 0.001). Also, the study discovered a substantial connection between the nurses’ familiarity to pain management and their ability to manage it in patients’ pain (β = 0.368, t = 6.619, p < 0.001). This study recommends that future research be undertaken to investigate the mediating effects of self-efficacy on the knowledge and attitude towards agony management relationship and distress management practices. Additionally, in future, scholars can examine the direct relationship between the effectiveness of agony control and pain management routines of nurses.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.352
Teacher spread0.333 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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