Un algoritmo di screening psicosociale dei nuclei familiari fragili afferenti alla AUSL di Modena

Titolo Rivista MALTRATTAMENTO E ABUSO ALL’INFANZIA
Autori/Curatori Carlo Foddis, Rosalba Di Biase, Daniele Di Girolamo, Beatrice Manfredi, Lucio Silingardi, Rossella Miglio, Luca Milani
Anno di pubblicazione 2024 Fascicolo 2023/3 Lingua Italiano
Numero pagine 24 P. 85-108 Dimensione file 358 KB
DOI 10.3280/MAL2023-003006
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La ricerca propone una prima validazione dell’algoritmo Screening Psicosociale Ri-schi/Risorse Parentali (SRP), sviluppato per supportare i Servizi di protezione dell’infanzia nella valutazione dei nuclei familiari afferenti. L’algoritmo SRP produce un output previsio-nale del rischio di esperienza infantili avverse (ACE) elaborando informazioni ricavate da: il Protocollo di valutazione dei fattori di rischio e di protezione psicosociale (FdR-FP); il Pa-renting Stress Index (PSI – SF); lo Strengths and Difficulties Questionnaire (SDQ). I partecipanti sono 122 minori (73 femmine; età media 9.31 anni; range = 0-17 aa; DS = 4.34). I risultati (V di Cramer 0.54; p-value associato al test Chi-quadrato < 0.001) mostrano buoni margini di efficacia previsionale dello strumento.;

Keywords:Adverse Childood Experiences; Decision making; Protocollo FdR-FP; Parent-ing Stress Index; Strengths and Difficulties Questionnaire.

  1. Cuccaro-Alamin, S., Foust, R., Vaithianathan, R., & Putnam-Hornstein, E. (2017). Risk assessment and decision making in child protective services: Predictive risk modeling in context. Children and Youth Service Review, 79, 291-298.
  2. Cummings, E. M., Davies, P. T., & Campbell, S. B. (2000). Developmental psychopathology and family process: Theory, research, and clinical implications. New York: Guilford Press.
  3. D’Andrade, A., Austin, M. J., & Benton, A. (2008). Risk and Safety Assessment in Child Walfare: Instrument Comparisons. Journal of Evidence-Based Social Work, Vol. 5, N.1/2, pp. 31-56.
  4. Dalgleish, L. I. (1988). Decision-making in child abuse cases: Applications of social judgement theory and signal detection theory. In: B., Joyce, C.R.B. (Ed.) Human judgment: The SJT view (pp.47-72). North Holland: Elsevier.
  5. Dalgleish, L. I. (2003). Risk, needs and consequences. In: Calder, M. C. (Ed.) Assessment in childcare: A comprehensive guide to frameworks and their use (pp. 86-99). Dorset, UK: Russell House Publishing.
  6. de Ruiter, C., Hildebrand, M., & van der Hoorn, S. (2020). The Child Abuse Risk Evaluation Dutch Version (CARE-NL): A retrospective validation study. Journal of Family Trauma, Child Custody & Child Development, 17(1), 37-57. DOI: 10.1080/15379418.2019.1699488
  7. Dettlaff, A., Graham, J. C., Holzman, J., Baumann, D. J., & Fluke, J. D. (2015). Development of an instrument to understand the child protective services decision-making process, with a focus on placement decisions. Child Abuse & Neglet, 49, 24-34.
  8. Dettlaff, A. J., Rivaux, S. R., Baumann, D. J., Fluke, J. D., Rycraft, J. R., & James, J. (2011). Disentangling substantiation: the influence of race, income & risk on the substantiation decision in child welfare. Children and Youth Service Review, 33, 1630-1637.
  9. Di Blasio, P., (2005). Tra rischio e protezione. La valutazione delle competenze parentali. Milano: Edizioni Unicopli.
  10. Di Riso, D., Salcuni, S., Chessa, D., Raudino, A., Lis, A., & Altoè, G. (2010). The Strengths and Difficulties Questionnaire (SDQ). Early evidence of its reliability and validity in a community sample of Italian children. Personality and Individual Differences, 49(6), 570-575.
  11. Dishion, T. J., Nelson, S. E., & Kavanagh, K. (2003). The Family Check-up with high-risk young adolescents: Preventing early-onset substance use by parent monitoring. Behavior Therapy, 34(4), 553-571. DOI: 10.1016/S0005-7894(03)80035-7
  12. Doueck, H. J., Levine, M., & Bronson, D. E. (1993). Risk assessment in Child Protective Services: An evaluation of the Child at Risk Field System. Journal of Interpersonal Violence, 8(4), 446-467. DOI: 10.1177/088626093008004002
  13. Font, S. A., & Maguire-Jack, K. (2015). Decision-making in child protective services: Influences at multiple levels of the social ecology. Child Abuse & Neglet, 47, 70-82.
  14. Galiano, A., Leogrande, A, Massari, S. F., & Massaro, A., (2019). I processi automatici di decisione: profili critici sui modelli di analisi e impatti nella relazione con i diritti individuali. Rivista Italiana di Informatica e Diritto, 2, 41-60. DOI: 10.32091/RIID0010.
  15. Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38, 581-586.
  16. Goodman, R. (2001), Psychometric properties of the Strengths and Difficulties Questionnaire (SDQ). Journal of the American Academy of Child and Adolescent Psychiatry. 40, 1337-1345. DOI: 10.1097/00004583-200111000-00015
  17. Grumi, S. Milani, L., & Di Blasio, P. (2017). Risk assessment in a multicultural context: Risk and protective factors in the decision to place children in foster care. Children and Youth Services Review, 77, 69-75.
  18. Guarino, A., Di Blasio, P., D'alessio, M., Camisasca, E., & Serantoni, G., (2008). Parenting Stress Index SF. Organizzazioni speciali, Firenze.
  19. Halverson, S. J., Kunju, L. P., & Bhalla, R. (2013) Accuracy of determining small renal mass management with risk stratified biopsies: confirmation by final pathology. J Urol., 189(2), 441-446.
  20. Hammond, K. (1996). Human judgment and social policy. New York: Oxford Univ. Press.
  21. Johnson, W. L. (2011). The validity and utility of the California Family Risk Assessment under practice conditions in the field: A prospective study. Child Abuse & Neglet, 35, 18-28.
  22. Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58(9), 697-720. DOI: 10.1037/0003-066X.58.9.697
  23. Kahneman, D., Frederick, S. (2005). A model of heuristic judgment. In Holyoak, K. & Morrison, B. (Eds), The Cambridge Handbook of Thinking and Reasoning (pp. 267-293). Cambridge: University Press.
  24. Keddell, E. (2021). Towards a critical decision-making ecology approach for child protection research. Qualitative Social Work. 20(5), 1141-1151. DOI: 10.1177/14733250211039064
  25. Kotu, V., Deshpande, B. (2015). Predictive Analytics and Data Mining. Amsterdam: Elsevier. DOI: 10.1016/B978-0-12-801460-8.00010-0
  26. Lèveillè, S., & Chamberland, C. (2010). Toward a general model for child welfare and protection services: A meta-evaluation of international experiences regarding the adoption of the Framework for Assessment of Children in Need and Their Families (FACNF). Children and Youth Services Review. 32(7), 929-944.
  27. Milani, L., Grumi, S., Camisasca, E., Miragoli, S., Traficante, D., Di Blasio, P. (2020). Familial risk and protective factors affecting CPS professionals’child removal decision: A decision tree analysis study. Children and Youth Services Review, 109, 1-8.
  28. Milani, L., Miragoli, S., Grumi, S., Di Blasio, P. (2019). A Multi-method Assessment of Risk and Protective Factors in Family Violence: Comparing Italian and Migrant Families. In Balvin, N., & Christie, D.J. (Eds). Children and Peace. Peace Psychology Book Series. DOI: 10.1007/978-3-030-22176-8_1
  29. Miragoli, S., Verrocchio, M. C. (2008). La valutazione del rischio in situazioni di disagio familiare: fattori di rischio e fattori di protezione. Maltrattamento e abuso all’infanzia, 10(3) 11-28. DOI: 10.1400/113420
  30. MiSE (2020). Proposte per una strategia italiana per l’Intelligenza Artificiale. Ministero dello Sviluppo Economico, Roma. -- Disponibile online: www.mimit.gov.it.
  31. Munro, E. (1999). Common errors of reasoning in child protection work. Child Abuse & Neglet, 23(8), 745-758.
  32. Pasceri, G. (2021). Intelligenza artificiale, algoritmo e machine learning. La responsabilità del medico e dell’amministrazione sanitaria. Milano: Giuffré Francis Lefebvre.
  33. Rahbar, H., Bhayani, S., Stifelman, M., Kaouk, J., Allaf, M., Marshall, S., & Rogers, C. (2014). Evaluation of renal mass biopsy risk stratification algorithm for robotic partial nephrectomy – could a biopsy have guided management?. The Journal of urology. 192(5), 1337-1342.
  34. Ranghetti, F., & Milani, L. (2022). Risk and Protective Factors regarding Child Neglect: Differences among Immigrant and Italian Parents. Journal of Aggression, Maltreatment & Trauma, 31(1), 44-54. DOI: 10.1080/10926771.2021.1970671
  35. Romani, F. (2017). Elementi di Algoritmica. Pisa: University Press.
  36. Schwartz, I. M., York, P., Nowakowski-Sims, E., & Ramos-Hernandez, A. (2017). Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward Country experience. Children and Youth Service Review, 81, 309-320.
  37. Shlonsky, A., & Wagner, D. (2004). The next step: Integrating actuarial risk assessment and clinical judgment into an evidence-based practice framework in CPS case management. Children and Youth Service Review, 27, 409-427.
  38. Sidebotham, P., Golding, J., & The ALSPAC Study Team (2000). Child maltreatment in the “Children of the Nineties”. A longitudinal study of parental risk factors. Child Abuse & Neglet, 25, 1177-1200. DOI: 10.1016/S0145-2134(01)00261-7
  39. Stokes, J., & Schmidt, G. (2012). Child Protection Decision Making: A Factorial Analysis Using Case Vignettes. Social Work, 57(1), 83-90.
  40. Swets, J. A., Tanner, W. P., & Birdsall, T. G. (1955). Decision processes in perception. Psychological Review, 68, 301-340. DOI: 10.1037/H0040547
  41. Thurston, H., & Miyamoto, S. (2018). The use of model based recursive partitioning as an analytic tool in child welfare. Child Abuse & Neglet, 79, 293-301.
  42. Tobia, V., Gabriele, M. A., & Marzocchi, G. M. (2011), Lo Strengths and Difficulties Questionnaire (SDQ) nella scuola primaria. Il comportamento dei bambini italiani valutato dai loro insegnanti. Disturbi di attenzione e iperattività. 6(2). Trento: Ed. Erickson.
  43. van Zyl, M. A., Barbee, A. P., Cunningham, M. R., Antle, B. F., Christensen, D. N., Boamah, D. (2014). Components of the Solution Based Casework child welfare practice model that predict positive child outcomes. Journal of Public Child Welfare. 8(4), 433-465. DOI: 10.1080/15548732.2014.939252
  44. Abidin, R. R. (1995). Parenting Stress Index (3rd ed.). Odessa, FL: Psychological Assessment Resources.
  45. Antle, B. F., Christensen, D. N., van Zyl, M. A., & Barbee, A. P. (2012). The impact of the Solution Based Casework (SBC), practice model on federal outcomes in public child welfare. Child Abuse & Neglect, 36, 342-353.
  46. Baird, C., & Wagner, D. (2000). The relative validity of actuarial- and consensus-based risk assessment systems. Children and Youth Service Review, 22, 839-871. DOI: 10.1016/S0190-7409(00)00122-5
  47. Baird, C., Wagner, D., Healy, T., & Johnson, K. (1999a). Research-based risk assessment: Adding equity to CPS decision-making. Madison, WI: Children’s Research Center.
  48. Baird, C., Wagner, D., Healy, T., Johnson, K. (1999b). Risk assessment in Child Protective Service: Consensus and Actuarial Model Reliability. Child Welfare League of America, 78(6), 723-748.
  49. Bartelink, C., van Yperen, T. A., & ten Berge, I. J. (2015). Deciding on child maltreatment: A literature review on methods that improve decision-making. Child Abuse & Neglet, 49, 142-151.
  50. Baumann, D. J., Dalgleish, L., Fluke, J., & Kern, H. (2011). The decision-making ecology. Washington, DC: American Humane Association.
  51. Bendenishty, R., & Chen, W. (2003). Decision making by the Child Protection Team of a Medical Center. Health & Social Work, 28(4), 284-292.
  52. Chang, J., Rhee, S., & Weaver, D. (2006). Characteristics of child abuse in immigrant Korean families and correlates of placement decisions. Child Abuse & Neglet, 30, 881-891.
  53. Cooksey, R. W. (1996). Judgment analysis: Theory, methods and application. Cambridge: Academic Press.
  54. Couchoud, C. G., Beuscart, J. B. R., Aldigier, J. C., Brunet, P. J., & Moranne, O. P. (2015). Development of a risk stratification algorithm to improve patient-centered care and decision making for incident elderly patients with end-stage renal disease. Kidney international, 88(5), 1178-1186.
  55. Ross, E. H., & Kearney, C. A. (2017). Posttraumatic symptoms among maltreated youth using classification and regression tree analysis. Child Abuse & Neglet, 69, 177-187.

Carlo Foddis, Rosalba Di Biase, Daniele Di Girolamo, Beatrice Manfredi, Lucio Silingardi, Rossella Miglio, Luca Milani, Un algoritmo di screening psicosociale dei nuclei familiari fragili afferenti alla AUSL di Modena in "MALTRATTAMENTO E ABUSO ALL’INFANZIA" 3/2023, pp 85-108, DOI: 10.3280/MAL2023-003006