Realities of opioid and gabapentinoid deprescribing in socioeconomically disadvantaged communities: a qualitative evaluation
Bibliographic record
Abstract
BACKGROUND: Opioid and gabapentinoid prescribing has increased substantially in recent years despite having limited effectiveness in treating chronic primary pain. This is concerning, with the prescribing rates and adverse effects of these medications being higher in more socioeconomically disadvantaged groups. Guidance for prescribing and deprescribing these medications exists but the understanding of how deprescribing is operationalised, especially in areas of socioeconomic disadvantage, is limited. AIM: To explore primary healthcare professionals' views and experiences of designing and implementing an intervention to reduce opioid and gabapentinoid prescribing. DESIGN & SETTING: A qualitative evaluation, using participant observation and semi-structured interviews with primary healthcare professionals, working in practices serving areas of substantial socioeconomic disadvantage in the North East of England. METHOD: Interviewees were purposively recruited with subsequent snowballing with participant observation of the peer-support meetings. Interview transcripts and notes from the participant observation were inductively coded and thematically analysed. RESULT: Thirteen healthcare professionals from five practices were interviewed. Person-centred care with shared decision-making was strived for, which was time-consuming owing to the complexity of the problem and patients. Where shared decision-making was not possible, owing to patient refusal or non-engagement, risk was used to determine the appropriate action. This work involved an emotional toll on staff and patients, but was at times conversely easier and more rewarding than expected. Ultimately, demedicalising pain with a culture change is required to ensure patients are not prescribed these medications for inappropriate reasons or doses. CONCLUSION: This study demonstrates key operational aspects to consider when undertaking opioid and gabapentinoid deprescribing in primary care, such as funding dedicated time to enable deprescribing.
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".