‘If you can’t see a dilemma in this situation you should probably regard it as a warning’: a metasynthesis and theoretical modelling of general practitioners’ opioid prescription experiences in primary care
Bibliographic record
Abstract
INTRODUCTION: The prescribing of opioids has increased internationally in developed countries in recent decades within primary and secondary care. The majority of patients with chronic non-malignant pain (CNMP) are managed by their general practitioner (GP). Recent qualitative studies have examined the issue of opioid prescribing for CNMP from a GP viewpoint. The aim of this study is to identify and synthesise the qualitative literature describing the factors influencing the nature and extent of opioid prescribing by GPs for patients with CNMP in primary care. METHODS: MEDLINE, Embase, PsycINFO, Cochrane Database, International Pharmaceutical Abstracts, Database of Abstracts of Reviews of Effects, CINAHL and Web of Science were systematically searched from January 1986 to February 2018. The full text of included articles was reviewed using the Critical Appraisal Skills Programme (CASP) tool for qualitative research. The papers were coded by two researchers and themes organised using Thematic Network Analysis. Themes were constructed in a hierarchical manner, basic themes informed organising themes which informed global themes. A theoretical model was derived using global themes to explain the interplay between factors influencing opioid prescribing decisions. RESULTS: From 7020 records, 21 full text papers were assessed, and 13 studies included in the synthesis; 9 were from the United States, 3 from the United Kingdom and 1 from Canada. Four global themes emerged: suspicion, risk, agreement and encompassing systems level factors. These global themes are inter-related and capture the complex decision-making processes underlying opioid prescribing whereby the physician both consciously and unconsciously quantifies the risk-benefit relationship associated with initiating or continuing an opioid prescription. CONCLUSION: Recognising the inherent complexity of opioid prescribing and the limitations of healthcare systems is crucial to developing opioid stewardship strategies to combat the rise in opioid prescription morbidity and mortality.
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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 | 0.103 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.032 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".