Theoretical Underpinnings of a Model to Reduce Polypharmacy and Its Negative Health Effects: Introducing the Team Approach to Polypharmacy Evaluation and Reduction (TAPER)
Bibliographic record
Abstract
BACKGROUND: Polypharmacy, particularly among older adults, is gaining recognition as an important risk to health. The harmful effects on health arise from disease-drug and drug-drug interactions, the cumulative burden of side effects from multiple medications and the burden to the patient. Single-disease clinical guidelines fail to consider the complex reality of optimising treatments for patients with multiple morbidities and medications. Efforts have been made to develop and implement interventions to reduce the risk of harmful effects, with some promising results. However, the theoretical basis (or pre-clinical work) that informed the development of these efforts, although likely undertaken, is unclear, difficult to find or inadequately described in publications. It is critical in interpreting effects and achieving effectiveness to understand the theoretical basis for such interventions. OBJECTIVE: Our objective is to outline the theoretical underpinnings of the development of a new polypharmacy intervention: the Team Approach to Polypharmacy Evaluation and Reduction (TAPER). METHODS: We examined deprescribing barriers at patient, provider, and system levels and mapped them to the chronic care model to understand the behavioural change requirements for a model to address polypharmacy. RESULTS: Using the chronic care model framework for understanding the barriers, we developed a model for addressing polypharmacy. CONCLUSIONS: We discuss how TAPER maps to address the specific patient-level, provider-level, and system-level barriers to deprescribing and aligns with three commonly used models and frameworks in medicine (the chronic care model, minimally disruptive medicine, the cumulative complexity model). We also describe how TAPER maps onto primary care principles, ultimately providing a description of the development of TAPER and a conceptualisation of the potential mechanisms by which TAPER reduces polypharmacy and its associated harms.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".