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Record W29809606

An examination of the efficacy of specific nursing interventions to the management of pain in cancer patients

2003· dissertation· en· W29809606 on OpenAlexaboutno aff
Verona Costello

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionCancer painIntervention (counseling)Nursing Interventions ClassificationPhysical therapyMedical-surgical nursingNursingPopulationPain assessmentCancerPain management
DOInot available

Abstract

fetched live from OpenAlex

Aim of the Study: The aim of this study was to determine if the nursing interventions of patient education and multidisciplinary coordination of care were able to improve pain control in the cancer patient in an acute hospital setting. Background of the Study: The role of the nurse in cancer pain management has been defined as being that of an educator, coordinator of care and advocate. A nurse with adequate knowledge of pain and its application to the cancer population and functioning in the role as defined is believed to be able to overcome many of the barriers that exist in implementing adequate analgesia and improve pain management in cancer patients. Design of the Study: A randomized experimental control group design was utilized. The study comprised 3 experimental groups and one control group incorporating pre and post testing. The Intervention of the Study: Experimental group one: subjects received education regarding their pain management which was tailored to meet their specific needs. Experimental group two: subjects underwent a pain assessment and construction of a care plan which was communicated verbally to the treating medical and nursing team and followed up with a written report which was documented in the history and sent to the treating medical physician. Experimental group three: subjects received the combined interventions administered to groups one and two. Control group four: subjects were assessed and all information was record in the same manner as for the experimental groups. The control group received their usual care during the study and their pain scores were measured at the same time intervals as the three experimental groups. Instrumentation: The Wisconsin Brief Pain Questionnaire was used for the assessment of all subjects. The McGill Pain Questionnaire was used as the outcome measure following intervention. Data Analysis: A one-way analysis of variance was used to detect the differences between the intervention groups and the control group. T-Tests were used to detect the differences between the groups incorporating a Bonferroni adjustment for frequent T tests. Results: The main effect demonstrated a significant difference between the treatment groups and control at a significance level of 0.002. T-Tests showed no significant difference between control and communication groups and no significant difference between education and combined groups. A significant difference was detected between education and control and between combined and control. Conclusions: Nursing interventions of patient education, coordination of care and advocacy can significantly improve cancer pain management. Intervention was tailored to meet the specific patient needs based on findings from the assessment and was dependent upon an adequate knowledge base. The nursing intervention of education was the most powerful of the three intervention types and its success was in tailoring to each individual. However, it is believed that with further recognition of the role of the nurse as coordinator of care will lead to greater improvements in cancer pain management.

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
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)0.0020.000

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 head0.025
GPT teacher head0.337
Teacher spread0.311 · 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 designSystematic review
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

Citations0
Published2003
Admission routes1
Has abstractyes

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