ArticleInternational journal of environmental research and public health2020
Causal Evidence and Dispositions in Medicine and Public Health.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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Who cites it
9 citing papers in PubMed.
- From Efficacy to Effectiveness: Evaluating Psychedelic Randomized Controlled Trials for Trustworthy Evidence-Based Policy and Practice.Pharmacology research & perspectives · 2025Review
- Randomized controlled trials as a source of evidence in rehabilitation: a critical analysis.European journal of physical and rehabilitation medicine · 2024Article
- Comment on "BIA-ALCL: Comparing the Risk Profiles of Smooth and Textured Breast Implants".Aesthetic plastic surgery · 2024Article
- The Value of Case Reports for Graduate Medical Education.Journal of graduate medical education · 2022Article
- The power of small data for personalized cancer care.Asia-Pacific journal of oncology nursing · 2022Article
- Dispositions and Causality Assessment in Pharmacovigilance: Proposing the Dx3 Approach for Assessing Causality with Small Data Sets.Pharmaceutical medicine · 2022Article
- COVID-19 and Dispositions of the Chilean Healthcare System: Sociomedical Networks in Care Decisions of Chronic Illnesses.Frontiers in sociology · 2021Article
- Withdrawal effects confounding in clinical trials: another sign of a needed paradigm shift in psychopharmacology research.Therapeutic advances in psychopharmacology · 2020Review
- Protracted withdrawal syndrome after stopping antidepressants: a descriptive quantitative analysis of consumer narratives from a large internet forum.Therapeutic advances in psychopharmacology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the introduction of evidence-based medicine, there have been discussions about the epistemic primacy of randomised controlled trials (RCTs) for establishing causality in medicine and public health. A growing movement within philosophy of science calls instead for evidential pluralism: that we need more than one single method to investigate health outcomes. How should such evidential pluralism look in practice? How useful are the various methods available for causal inquiry? Further, how should different types of causal evidence be evaluated? This paper proposes a constructive answer and introduces a framework aimed at supporting scientists in developing appropriate methodological approaches for exploring causality. We start from the philosophical tradition that highlights intrinsic properties (dispositions, causal powers or capacities) as essential features of causality. This abstract idea has wide methodological implications. The paper explains how different methods, such as lab experiments, case studies, N-of-1 trials, case control studies, cohort studies, RCTs and patient narratives, all have some strengths and some limitations for picking out intrinsic causal properties. We explain why considering philosophy of causality is crucial for evaluating causality in the health sciences. In our proposal, we combine the various methods in a temporal process, which could then take us from an observed phenomenon (e.g., a correlation) to a causal hypothesis and, finally, to improved theoretical knowledge.
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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.