Reasons for non‐use of proven pharmacotherapeutic interventions: systematic review and framework development
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The quality of patient care and safety is dependent on addressing both errors of commission (e.g. overuse of medications) and errors of omission (e.g. patients receiving too little care). Despite guidelines recommending the use of certain proven pharmacotherapeutic interventions, a large gap exists between the patients that have an indication for, and those that actually receive such interventions. To address how the rate of implementation of proven interventions can be improved is dependent on a comprehensive knowledge of the factors contributing to their underuse. The aim of the review is to create an evidence-based framework of reasons why eligible patients do not receive proven pharmacotherapeutic interventions. METHODS: A systemic review of the published reasons for non-use based on the Cochrane methodology. RESULTS: The systematic review identified 67 articles meeting the inclusion criteria. The reasons for non-use were extracted from the studies and a framework was created from the results. CONCLUSIONS: The factors associated with lack of implementation of proven pharmacotherapeutic interventions are complex and heterogeneous but can be understood from the perspectives of clinicians, patients and health care delivery systems. Efforts to increase the utilization of proven interventions should focus on disease/intervention-specific programmes that take into account the identified modifiable clinician, patient and system factors.
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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 | 0.119 | 0.279 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".