Healthcare provider knowledge, attitudes, beliefs, and practices surrounding the prescription of opioids for chronic non-cancer pain in North America: protocol for a mixed-method systematic review
Bibliographic record
Abstract
BACKGROUND: Evidence from diverse areas of medicine (e.g., cardiovascular disease, diabetes) indicates that healthcare providers (HCPs) often do not adhere to clinical practice guidelines (CPGs) despite a clear indication to implement recommendations-a phenomenon commonly termed clinical inertia. There are a variety of reasons for clinical inertia, but HCP-related factors (e.g., knowledge, motivation, agreement with guidelines) are the most salient and amenable to intervention aimed to improve adherence. CPGs have been developed to support the safe and effective prescription of opioid medication for the management of chronic non-cancer pain. The extent of physician uptake and adherence to such guidelines is not yet well understood. The purpose of this review is to synthesize the published evidence about knowledge, attitudes, beliefs, and practices that HCPs hold regarding the prescription of opioids for chronic non-cancer pain. METHODS: An experienced information specialist will perform searches of CINAHL, Embase, MEDLINE, and PsycINFO bibliographic databases. The Cochrane library, PROSPERO, and the Joanna Briggs Institute will be searched for systematic reviews. Searches will be performed from inception to the present. Quantitative and qualitative study designs that report on HCP knowledge, attitudes, beliefs, or practices in North America will be eligible for inclusion. Studies reporting on interventions to improve HCP adherence to opioid prescribing CPGs will also be eligible for inclusion. Two trained graduate-level research assistants will independently screen articles for inclusion, perform data extraction, and perform risk of bias and quality assessment using recommended tools. Confidence in qualitative evidence will be evaluated using the Grades of Recommendation, Assessment, Development, and Evaluation-Confidence in the Evidence from Qualitative Reviews (GRADE-CERQual) approach. Confidence in quantitative evidence will be assessed using the GRADE approach. DISCUSSION: The ultimate goal of this work is to support interventions aiming to optimize opioid prescribing practices in order to prevent opioid-related morbidity and mortality without restricting a HCP's ability to select the most appropriate treatment for an individual patient. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018091640 .
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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 | 0.095 | 0.083 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.021 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
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".