33 Advancing de-implementation theory: the magnitude of the problem conceptual framework for integrated sustainability strategies
Bibliographic record
Abstract
In addition to reducing patient harms and wasted resources, efforts to reduce low-value care produce significant climate and population health benefits, but these benefits are typically not elucidated or included in intervention design or implementation. There are increasing calls for ‘integrated sustainability strategies’ that simultaneously address climate and other critical challenges facing communities, while securing ‘co-benefits’. Integrated sustainability strategies are actions that can be taken to achieve multiple health, climate, social and economic objectives. Co-benefits are positive effects that a policy or measure aimed at one objective might have on other objectives, thereby increasing the total benefit for society. For health systems addressing these challenges, addressing low-value care has the potential to be a critically important integrated sustainability strategy, also securing climate and ecological, and population health co-benefits at the frontline of care delivery, and at organization and system levels. Approaches to reducing low-value care that use an integrated sustainability strategy have the potential to produce ‘win-win’ situations that broaden engagement, relevance, and impact. Co-benefits are often unplanned ‘happy accidents’, but these benefits can be deliberately optimizing through understanding interdependent relationships, identifying synergies, and addressing potential barriers. To support work to develop and implement integrated sustainability strategies, the objective of this study was to develop a conceptual framework to collect and map data on the harms, resources and prevalence of the magnitude of the problem across low-value care, climate and ecological and population health. We argue that a deeper understanding of the complex and nuanced factors supporting and sustaining inappropriate/harmful practices and the subsequent environmental and population health harms, resources and prevalence is a critical first step to identifying the scope and synergies between these often silo-ed initiatives. The data collected through the conceptual framework will support planning, root cause analysis and development of appropriately targeted integrated sustainability strategies. We use inappropriate antibiotic prescribing and its downstream implications, environmental impacts and antimicrobial resistance, to illustrate the application of the conceptual framework. Inappropriate antibiotic prescribing is a global problem that affects human, animal and plant health. Downstream environmental impacts include complex and hazardous solid, air and water emissions, including toxic waste. Antimicrobial resistance increases morbidity and mortality, is associated with high economic costs due to its health care burden and plays a complex role in social and global health inequalities. Addressing inappropriate antibiotic prescribing and antimicrobial resistance through integrated sustainability solutions has the potential to yield significant clinical, climate and ecological, population health co-benefits. This framework, which builds on concepts in Norton, Chamber & Kramer’s (2019) framework to conceptualize de-implementation in cancer care delivery, will be used in the next phase of our research to support the development of integrated sustainability strategies to realize the myriad clinical, climate and ecological and population health co-benefits of reducing low-value care.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".