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Record W4398768144 · doi:10.3390/curroncol31060225

Identifying Barriers to Effective Cancer Pain Management in Oman: Implications for Palliative Care

2024· article· en· W4398768144 on OpenAlexvenueno aff
Husain Ali Alaswami, Atika AlMusalami, M. Saadi, Adhari AlZaabi

Bibliographic record

VenueCurrent Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersSultan Qaboos University
KeywordsMedicinePalliative careCancer painPain managementCancerAlternative medicineFamily medicineNursingPhysical therapyPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Effective cancer pain management is essential for improving the quality of life of patients. However, the use of analgesics is often suboptimal due to various patient-related barriers. This study aims to explore the perceptions, knowledge, and attitudes toward analgesic use among cancer patients in Oman, which may influence their pain management strategies. METHODS: In a cross-sectional study, we assessed 68 cancer patients undergoing pain management at an inpatient cancer clinic of a tertiary hospital in Oman from a pool of 154 eligible participants. The Barriers Questionnaire (BQ) and the Patient Pain Questionnaire (PPQ), both Arabic versions, were administered to evaluate the patients' barriers to cancer pain management. The study period and the criteria for patient selection are specified. RESULTS: With a participation rate of 44.2% and a female-to-male ratio of 2.28:1, the mean score on the BQ was 2.52 (SD 0.84), indicating a moderate level of perceived barriers. Patients' scores suggested notable barriers, with older patients exhibiting reluctance toward analgesics for fear of masking symptoms and female patients expressing greater concerns about developing drug tolerance. CONCLUSION: The findings highlight significant attitudinal barriers to effective cancer pain management in Oman, notably a prevalent fear of medication tolerance. The study stresses on the need for targeted patient education and the correction of misconceptions. It also points to the influence of cultural and religious beliefs on patient responses, advocating for the implementation of culturally sensitive, evidence-based pain management guidelines, and the support of multidisciplinary palliative care teams.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.094
GPT teacher head0.478
Teacher spread0.384 · 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 designOther design
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

Citations7
Published2024
Admission routes1
Has abstractyes

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