Prevalence and management of pain disorders among patients of different stages of life at cape coast teaching hospital, Ghana
Bibliographic record
Abstract
Introduction: Pain significantly impacts quality of life, yet healthcare providers in resource-limited settings such as Ghana often struggle to detect and manage pain sufficiently. This study examines pain prevalence and management strategies across different age groups in a Ghanaian teaching hospital. Method: A cross-sectional, mixed-methods study was carried out at Cape Coast Teaching Hospital (CCTH). The study considered categories of participants: infants, children and adults. Data were collected using structured questionnaires. The FLACC scale rated infant pain, the Wong-Baker FACES measured child pain, and the McGill Pain Questionnaire assessed adult pain. Results: Distinct patterns of pain experience across different age groups were observed. Among infants, 70.9% experienced pain, with malaria (16.7%), car accidents (14.1%) and jaundice (11.5%) as the main causes. Infants experienced acute pain the most, affecting 80%. Among children, abdominal pain (61.6%) was the leading form of pain among those who experienced pain (58.6%). For adults, 60% reported experiencing pain mainly due to ongoing medical problems (28.7%). Adults experienced more chronic pain episodes (56.0%). Across all age groups, pharmacotherapy was the primary treatment approach, with paracetamol and ibuprofen being the most prescribed pain relievers. Treatment outcomes varied, with 25.0% experiencing complete relief, 29.0% partial relief and 45.9% reporting no relief. Conclusion: The research data illustrate widespread pain among all age categories, thus demanding specialized assessment instruments and tailored management solutions for various patient age groups. Pain relief was mostly pharmacological, but many chronic pain patients reported only temporary relief. The study emphasizes the need for pain clinics with suitable age-appropriate management strategies, advocating patient-centered care to improve treatment outcomes and quality of life.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".