Impact of pharmacist-delivered interventions on pain-related outcomes: An umbrella review of systematic reviews and meta-analyses
Bibliographic record
Abstract
INTRODUCTION: Pain is a significant healthcare challenge, impacting millions worldwide. Pharmacists have increasingly taken on expanded roles in managing pain, particularly in primary and ambulatory care contexts. This umbrella review aims to systematically evaluate evidence from published systematic reviews that explore the impact of pharmacist-delivered interventions on clinical, humanistic, and economic outcomes related to pain. METHODS: A systematic search was conducted across six electronic databases, including Ovid Embase, MEDLINE, CINAHL, Scopus, CENTRAL, APA PsycINFO, and DARE, from inception until June 2023. Prior to inclusion, two independent reviewers assessed study titles and abstracts. Following inclusion, an assessment of the methodological quality of the included studies was conducted. AMSTAR 2 was used to evaluate the methodological quality of the included SRs. RESULTS: From 2055 retrieved titles, 11 systematic reviews were included, with 5 out of 11 being meta-analyses. These SRs encompassed diverse pharmacist-led interventions such as education, medication reviews, and multi-component strategies targeting various facets of pain management. These findings showed favorable clinical outcomes, including reduced pain intensity, improved medication management, enhanced overall physical and mental well-being, and reduced hospitalization durations. Significant pain intensity reductions were found due to pharmacists' interventions, with standardized mean differences (SMDs) ranging from -0.76 to -0.22 across different studies and subgroups. Physical functioning improvements were observed, with SMDs ranging from -0.38 to 1.03. Positive humanistic outcomes were also reported, such as increased healthcare provider confidence, patient satisfaction, and quality of life (QoL). QoL improvements were reported, with SMDs ranging from 0.29 to 1.03. Three systematic reviews examined pharmacist interventions' impact on pain-related economic outcomes, highlighting varying cost implications and the need for robust research methodologies to capture costs and benefits. CONCLUSION: This umbrella review highlights the effectiveness of pharmacist-delivered interventions in improving clinical, humanistic, and economic outcomes related to pain management. Existing evidence emphasises on the need to integrate pharamacists into multi-disciplinary pain management teams. Further research is needed to investigate innovative care models, such as pharmacist-independent prescribing initiatives within collaborative pain management clinics.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".