Adult cancer patients’ barriers toward pain management: A literature review
Bibliographic record
Abstract
Background and objective: Cancer pain is the most common symptom among cancer patients. Despite strategies to control cancer pain, cancer patients’ beliefs and attitudes influence the effectiveness of cancer pain management. The aim of this literature review was to identify and explore adult cancer patients’ barriers toward pain management.Methods: A literature review was conducted. CINAHL, Medline, and PsychINFO databases were searched for relevant articles from 2008 to 2019. Twenty one articles were included in this literature review. Thematic analysis was conducted to identify and explore adult cancer patients’ barriers toward pain management. Results: This literature review revealed several patient barriers toward pain management. These barriers were categorized into cognitive barriers that include poor pain communication, fatalism, and fear of addiction and tolerance; sensory barrier, such as fear of drug side effects; affective barriers, such as anxiety and depression, and socio-demographic barriers that influence cancer pain management.Conclusions: Adult cancer patients’ barriers toward pain management significantly compromise the effectiveness of pain management and affect cancer patients’ quality of life. A better understanding of cancer patients’ barriers toward pain management by the healthcare providers will result in better assessment and management of these barriers and will enhance evidenced-based patient education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".