Swallowing the pill of adverse effects: A qualitative study of patients' and pharmacists' experiences and decision‐making regarding the adverse effects of chronic pain medications
Bibliographic record
Abstract
INTRODUCTION: Pharmacological treatments of chronic pain can lead to numerous and sometimes serious adverse effects. Drawing on a social science approach to chronic illness, this study aimed to understand the experiences of people living with chronic pain and community pharmacists regarding the definition, prevention and management of analgesic adverse effects. METHODS: This qualitative study proceeded through 12 online focus groups (FGs) with people living with chronic pain (n = 26) and community pharmacists (n = 19), conducted between July 2020 and February 2021 in the province of Quebec, Canada. The semistructured discussion guides covered participants' definitions of adverse effects and decision-making regarding their prevention and management. Discussions were audio-recorded, transcribed verbatim and analysed using grounded theory. RESULTS: Both people with chronic pain and pharmacists provided varying definitions of analgesic adverse effects depending on patients' social and clinical characteristics. Present quality of life and serious long-term risks related to treatment were described as key dimensions influencing adverse effect appraisal. Dilemmas and discrepancies occurred between patients and pharmacists when choosing to prioritize pain relief or adverse effect prevention. Some patients lacked information about their medications and wanted to be more involved in decisions, while many pharmacists were concerned by patients' self-management of adverse effects. Preventing opioid-related overdoses often led pharmacists to policing practices. Despite most pharmacists wishing they could have a key role in the management of pain and adverse effects face organizational and financial barriers. CONCLUSION: Defining, preventing and managing adverse effects in the treatment of chronic pain requires a person-centred approach and shared decision-making. Clinical training improvements and healthcare organization changes are needed to support pharmacists in providing patients with community-based follow-up and reliable information about the adverse effects of chronic pain treatments. PATIENT OR PUBLIC CONTRIBUTION: A person with lived experience of chronic pain was involved as a coinvestigator in the study. He contributed to shaping the study design and objectives, including major methodological decisions such as the choice of pharmacists as the most appropriate professionals to investigate. In addition, 26 individuals with chronic pain shared their experiences extensively during the FGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".