Salta al menu principale di navigazione Salta al contenuto principale Salta al piè di pagina del sito

Giornate Franco Fasolo 2024: Qualcosa non è cambiato. La clinica analitico-gruppale al setaccio dei fallimenti terapeutici

N. 1 (2024)

La dimensione sociale nella costruzione della conoscenza: la scienza tra evidenza e valori

DOI
https://doi.org/10.3280/gruoa1-2024oa22690
Inviata
aprile 29, 2026
Pubblicato
2026-05-13

Abstract

Che cosa significa costruire conoscenza scientifica rispetto alla salute mentale? Quali impegni vengono assunti? Quali sono le evidenze da raccogliere e come devono essere trattate? In questo contributo verranno presentati alcuni suggerimenti provenienti dalla filosofia della scienza, volti a chiarire le possibili declinazioni della nozione di “malattia mentale” e del concetto di “oggettività scientifica”, e a sottolineare i ruoli che i valori – epistemici e non – possono assumere nell’elaborazione di teorie e modelli. Dalla discussione di possibili intersezioni e interazioni concettuali e metodologiche con la filosofia della scienza, si intende far emergere un quadro delle discipline della salute mentale come sapere al tempo stesso fallibile e affidabile.

Riferimenti bibliografici

  1. Campaner R. (2011). Understanding Mechanisms in the Health Sciences. Theoretical Medicine & Bioethics, 32, 1: 5-17. DOI: 10.1007/s11017-010-9166-5
  2. Carrier M. (2010). Scientific Knowledge and Scientific Expertise: Epistemic and Social Conditions of Their Trustworthiness. Analyse and Kritik, 32, 2: 195-212. DOI: 10.1515/auk-2010-0201
  3. Chang H. (2017). Epistemic Iteration and Natural Kinds: Realism and Pluralism in Taxonomy. In: Kendler K. and Parnas J., eds., Philosophical Issues in Psychiatry IV: Classification of Psychiatric Illness. Oxford: Oxford University Press.
  4. Deserno M.K., Bathelt J., Groenman A.P. e Geurts H.M. (2023). Probing the Overarching Continuum Theory: Data Driven Phenotypic Clustering of Children with ASD or ADHD. European Child & Adolescent Psychiatry, 32: 1909-1923. DOI: 10.1007/s00787-022-01986-9
  5. Douglas H. (2007). Rejecting the Ideal of Value-Free Science. In: Kincaid H., Dupré J. e Wylie A., Eds. Value-Free Science? Ideals and Illusions. Oxford: University Press.
  6. Giere R. (2006). Scientific Perspectivism. Chicago: The University of Chicago Press.
  7. Glennan S. (2017). The New Mechanical Philosophy. Oxford: Oxford University Press.
  8. Hennig C. (2015). What are the True Clusters? Pattern Recognition Letters, 64: 53-62. DOI: 10.1016/j.patrec.2015.04.009
  9. Insel (2014). The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry. American J. of Psych., 171, 4: 395-397. DOI: 10.1176/appi.ajp.2014.14020138
  10. Jacobs G.R., Voineskos A.N., Hawco C., Stefanik L., Forde N.J., Dickie E.W., Meng-Chuan Lai, Szatmari P., Schachar R., Crosbie J., Arnold P.D., Goldenberg A., Erdman L. e Ameis S.H. (2021). Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting across Children with ASD, ADHD or OCD. Neuropsychopharmacology, 46: 643-653. DOI: 10.1038/s41386-020-00902-6
  11. Kincaid H., Dupré J. e Wylie A. (2007). Value-Free Science? Ideals and Illusions. Oxford: Oxford University Press
  12. Leonelli S. (2015). What Counts as Scientific Data? A Relational Framework. Philosophy of Science, 82: 810-821. DOI: 10.1086/684083
  13. Longino H. (1990). Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton: Princeton University Press.
  14. Longino H. (2002). The Fate of Knowledge. Princeton: Princeton University Press.
  15. Machamer P., Darden L. e Craver C. (2000). Thinking about Mechanisms. Philosophy of Science, 67, 1: 1-25. DOI: 10.1086/392759
  16. Morris S.J. (2023). An Individualized, Data-Driven Biological Approach to Attention Deficit/Hyperactivity Disorder (ADHD) Heterogeneity. Research on Child and Adolescent Psychopathology, 51: 1565-1579. DOI: 10.1007/s10802-023-01104-6
  17. Murphy D. (2009). Psychiatry and the Concept of Disease as Pathology. In: Broome M.R. e Bortolotti L., Eds. Psychiatry as Cognitive Neuro-science. Philosophical perspectives. OUP Oxford.
  18. Parlett-Pelleriti C.M., Stevens E., Dixon D. e Linstead E.J. (2023). Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review. Review J. of Autism and Developmental Disorders, 10: 406-421. DOI: 10.1007/s40489-021-00299-y
  19. Rivard M., Mestari Z., Morin D., Coulombe P., Mello C. e Morin M. (2023). Cluster Analysis of Clinical Features of Children Suspected to Have Neurodevelopmental Disorders. J. Autism. Dev. Disord., 53: 2409-2420. DOI: 10.1007/s10803-022-05533-y
  20. Setyawan J., Fridman M., Grebla R., Harpin V., Korst L.M. e Quintero J. (2018). Variation in Presentation, Diagnosis, and Management of Children and Adolescents with ADHD Across European Countries. J. of Attention Disorders, 22, 10: 911-923. DOI: 10.1177/1087054715597410
  21. Stein D.J., Nielsen K., Hartford A., Gagné-Julien A.M., Glackin S., Friston K., Maj M., Zachar P. e Aftab A. (2024). Philosophy of Psychiatry: Theoretical Advances and Clinical Implications. World Psychiatry, 23: 215-232. DOI: 10.1002/wps.21194
  22. Trevithick L., Painter J. e Keown P. (2015). Mental Health Clustering and Diagnosis in Psychiatric In-Patients. BJPsych Bull., Jun., 39, 3: 119-123. DOI: 10.1192/pb.bp.114.047043
  23. Widding Havneraas T., Markussen S., Elwert F., Lyhmann I., Bjelland I., Halmøy A., Chaulagain A., Ystrom E., Mykletun A. e Zachrisson H.D. (2023). Geographical Variation in ADHD: do Diagnoses Reflect Symptom Levels? European Child & Adolescent Psychiatry, 32: 1795-1803. DOI: 10.1007/s00787-022-01996-7
  24. Wylie A. e Hankinson Nelson L. (2007). Coming to Terms with the Value(s) of Science: Insights from Feminist Science Scholarship. In: Kincaid H., Dupré J. e Wylie A., Eds. Value-Free Science? Ideals and Illusions. Oxford: Oxford University Press.
  25. Zhang M., Huang Y., Jiao J., Yuan, D., Hu X., Yang P., Zhang R., Wen L., Situ M., Cai J., Sun X., Guo K., Huang X. e Huang Y. (2022). Transdiagnostic Symptom Subtypes across Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder: Validated by Measures of Neurocognition and Structural Connectivity. BMC Psychiatry, 22: 102 (2022). DOI: 10.1186/s12888-022-03734-4

Metriche

Caricamento metriche ...