A-I-D for Cascades: Designing a theory-informed intervention for addressing prescribing cascades in primary care
Bibliographic record
Abstract
Abstract Introduction Prescribing cascades, which occur when a medication is used to treat the side effect of another medication, are important contributors to polypharmacy. There is an absence of studies that evaluate the implementation or impact of existing interventions to address prescribing cascades in practice. Aim To design theory-informed options for interventions to address prescribing cascades within interprofessional primary care teams. Methods The Behaviour Change Wheel (BCW) framework, and its eight steps, were applied to guide intervention development by the research team. Three target behaviours were drafted and prioritised for intervention development based on data collected as part of two qualitative studies exploring why and how cascades occur across practice settings.[1,2] A target behaviour was selected and the COM-B (capability, opportunity, motivation-behaviour) model was then applied to identify the relevant factors for interprofessional primary care teams. The BCW was used to determine the relevant intervention types and policy options for the behaviour. Next, corresponding behaviour change techniques (BCTs) were identified and intervention options drafted. Prioritisation of behaviours and intervention examples were guided by the APEASE criteria (Affordability, Practicability, Effectiveness/cost-effectiveness, Acceptability, Side-effects/safety, Equity). Results The three target behaviours involved supporting: 1) healthcare providers to ask about, investigate and manage cascades (often through deprescribing), 2) the public to ask about prescribing cascades, and 3) the public to share medication histories and experiences with healthcare providers. The team selected the healthcare provider behaviour, called A-I-D (ask, investigate, deprescribe), for intervention development. Psychological capability and physical opportunity were determined to be the most relevant COM-B components, corresponding to education, training, environmental restructuring, and enablement intervention types and the guidelines, communications and marketing, and service provision policy options within the BCW model. Ultimately, 10 intervention options comprised of BCTs were developed by the team, which are ready for further prioritisation by stakeholders. These can be grouped into three categories: provision of educational content or materials for use by clinicians, provision of consultation or training to support clinicians, and knowledge mobilisation strategies. Through the process, the team identified that development of a practice guidance tool, which assists healthcare providers to investigate and manage prescribing cascades, is needed to support further intervention development. Conclusion The BCW framework guided the design of intervention options that will support primary care clinicians practising in interprofessional teams to address prescribing cascades. A limitation of this work is that applying the BCW framework required several judgements by the team, comprised of scientists, pharmacists, and nurses but not a general practitioner physician. Many but not all have practised with primary care interprofessional teams. When identifying interventions for future consultation, it was determined that the development of a practice guidance tool (i.e., which assists with identifying, investigating, and managing prescribing cascades) underpinned all the proposed interventions for addressing prescribing cascades in practice. Further research is needed to determine what primary care clinicians will need in this practice guidance tool and how it will be used in practice, to support its development. References 1. Farrell BJ, Jeffs L, Irving H et al. Patient and provider perspectives on the development and resolution of prescribing cascades: a qualitative study. BMC Geriatr 2020;20:368. 2. Farrell B, Galley E, Jeffs L, Howell P, McCarthy LM. “Kind of blurry”: Deciphering clues to prevent, investigate and manage prescribing cascades. PLoS ONE 2022;17(8): e0272418.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".