ArticleBJPsych open2025
Beyond all-or-nothing: why binary thinking undermines harm reduction in addiction medicine.
Article in BJPsych open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern healthcare, decision-making favours neatly delineated, categorical imperatives. We prefer to say: 'This practice is good' and 'That one is bad', believing that each decision has a straightforward yes-or-no resolution. However, medicine thrives in uncertainty, partial improvements and small steps that can lead to life-altering gains. Harm reduction, whether for tobacco use, opioid dependence or beyond, embodies the acceptance of imperfect solutions. It is precisely in these areas that black-or-white thinking can be most destructive. Insisting on total cessation or complete eradication of risk, rather than supporting incremental progress, alienates many patients and perpetuates preventable morbidity and mortality. Recognising this pattern and transcending 'all-or-nothing' mindsets is crucial for compassionate, evidence-based care. Accordingly, we ask: 'How does binary thinking in medical decision-making impact the effectiveness of harm reduction strategies?' Such an inquiry addresses how well we can truly meet patient needs in real-world practice, especially amid complexity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.