of associated Stigmatization in mental health professionals: characteristics and associated factors The adaptation of The parental reflective functioning questionnaire adolescent version to the Hungarian language and presentation of its psychometric characteristics

suicidal ideation and lower proportions of affective, psychosis and suicidal/self-injurious acts during the pandemic period compared to before, p = 0.006. The ratio of female to male patients on the other hand were similar during both periods, p = 0.853. There appeared to be no difference in median time to follow-up pre and during the pandemic (6.0 vs 5.5 days, p = 0.995). Further analysis also found no significant impact on time to follow-up upon implementing telemedicine consultations, with median days to initial follow-up of 6 days pre-pandemic, 4.5 days during pandemic þ prior to telemedicine and 6.5 days during pandemic þ telemedicine, p = 0.602. Conclusions: This study provides preliminary data on the impact of COVID-19 on mental health emergency presentations and utilization of telemedicine on time to follow-up by CMHTs. Disclosure: No significant relationships. then conducted among users and family members to characterize these situations and identify predictors. Results: A total of 235 participants were included: 59 participants with schizophrenia diagnosis, 96 with other psychiatric diagnoses and 80 family members. The results revealed 15 situations with different levels of frequency, stigmatization and suffering. Participants with a diagnosis of schizophrenia experienced more situations of stigmatization and with a higher frequency. Moreover, factors such as recovery-oriented practices and measures without consent were the best predictors of experienced stigmatization. Conclusions: These original stigmatization situations could be targeted to reduce stigmatization and associated suffering in mental health practices. Results strongly suggest that recovery-oriented practice should be fostered to fight stigma in mental health care. Disclosure: No significant relationships. prejudice) in MHP are of two types: i) individual beliefs (about mental illness: biological etiological beliefs, categorical beliefs; or about MHP them-selves: professional utility beliefs, similarity beliefs) and ii) characteristics of practices (recovery oriented practice, work set-ting, profession). Conclusions: These original results suggest new strategies for reducing stigma in mental health practices such as focusing on individual beliefs and fostering recovery-oriented practice and professional utility beliefs. Disclosure: No significant relationships. Objectives: The aim of our research was to adapt the adolescent version of The Parental Reflective Functioning Questionnaire to the Hungarian language. Methods: In our study 240 mothers completed the adolescent version of The Parental Reflective Functioning Questionnaire (PRFQ-A), and the Reflective Function Questionnaire (RFQ). Results: Confirmatory factor analysis did not confirm the original three-factor structure. The principal component analysis resulted in a two-factor structure. Factors corresponded to the original questionnaire ’ s certainty in mental states (Alpha = .81) and interest and curiosity subscales (Alpha = .70). When analyz-ing the relationship between parental reflective function and reflective function, the subscales of the parental reflective function questionnaire were examined with two types of median coding in addition to polar coding. During the first median coding, the frequency of scores in the middle of the scales reflected optimal mentalization, while the frequency of extreme values on the scales corresponded to less favorable reflective functioning. With the second median coding, hypermentaliza-tion and hypomentalization subscales were also created. The second median transcoding proved to be the most suitable for capturing the relationship between RFQ and PRFQ-A. Conclusions: The questionnaire proved to be a reliable measure on the Hungarian sample and we recommend using the additional subscales. Disclosure: No significant relationships. symptoms. Lurasidone is an atypical antipsychotic approved in Spain for the treatment of schizophrenia in September 2019. An RWD-based picture of lurasidone use is necessary to better understand its impact in routine clinical practice. Objectives: To set up a methodology based on Natural Language Processing (NLP) and machine learning for the analysis of the free-text information contained in the EHRs of patients treated with lurasidone in Spain. Methods: A multicenter, retrospective study based on RWD collected in EHRs of lurasidone users will be conducted in hospitals from the Spanish National Healthcare System. Information extracted from the free text in EHRs using NLP will be treated and analyzed as big data. Results: A study database for lurasidone-treated patients in Spain has been instituted using the EHRead® technology ( Figure 1 ), which applies machine learning and deep learning to extract, analyze, and interpret the free-text information written in their de-identified EHRs. Sociodemographic and clinical variables in EHRs from September 2019 until the most recent data available are being collected to describe the target patient population and address treatment-related outcomes. Conclusions: NLP of free text in EHRs of lurasidone-treated patients renders a real-world picture of lurasidone usage in Spain. Studies using artificial intelligence techniques represent a novel source of information regarding psychiatric disorders and their clinical management. Disclosure: I. Gabarda is employee at Angelini Pharma España, S.L.U. and C. de la Pinta is employee at Medsavana.

Introduction: Mental health care is considered to be one of the main sources of mental illness stigmatization. Detailed information about these stigmatization experiences is needed to reduce stigma in mental health practices. Objectives: The study aimed i) to identify the most relevant stigmatizing situations in mental health care encountered by users and families, ii) to characterize the relative importance of these situations in terms of frequency, experienced stigmatization and suffering, and iii) to identify individual and contextual factors associated with these experiences. Methods: In a focus group, users were asked to select the 15 most relevant stigmatization situations among those they elicited and those that were taken from the literature. An online survey was then conducted among users and family members to characterize these situations and identify predictors. Results: A total of 235 participants were included: 59 participants with schizophrenia diagnosis, 96 with other psychiatric diagnoses and 80 family members. The results revealed 15 situations with different levels of frequency, stigmatization and suffering. Participants with a diagnosis of schizophrenia experienced more situations of stigmatization and with a higher frequency. Moreover, factors such as recovery-oriented practices and measures without consent were the best predictors of experienced stigmatization. Conclusions: These original stigmatization situations could be targeted to reduce stigmatization and associated suffering in mental health practices. Results strongly suggest that recovery-oriented practice should be fostered to fight stigma in mental health care. Introduction: The consequences of schizophrenia stigma are numerous and highly damaging to individuals, their families, the health care system and society. Mental health professionals (MHP) are considered to be one of the main sources of schizophrenia stigmatization.
Objectives: The aim of the study was to identify individual and contextual factors associated with stigmatization in MHP in its three dimensions. Methods: An online survey was conducted with specific measures of MHP stigmatization (stereotypes, prejudices and discrimination). Four categories of potential associated factors were also measured: sociodemographic information, contextual characteristics (e.g. work setting), individual characteristics (e.g. profession, recovery-oriented practices) and theoretical beliefs (e.g. biological beliefs, perceived similarities, continuum beliefs). Models of prediction were computed when applicable. Results: Responses of 357 MHP were analysed. The main factors associated with stigmatization (stereotypes, prejudice) in MHP are of two types: i) individual beliefs (about mental illness: biological etiological beliefs, categorical beliefs; or about MHP themselves: professional utility beliefs, similarity beliefs) and ii) characteristics of practices (recovery oriented practice, work setting, profession). Conclusions: These original results suggest new strategies for reducing stigma in mental health practices such as focusing on individual beliefs and fostering recovery-oriented practice and professional utility beliefs. Introduction: Parental reflective function is the ability of a parent to attribute mental states to their child and to themselves. The Parental Reflective Functioning Questionnaire is widely used for the measurement of this construct, the adolescent version of which can be used by parents of children aged 12-18.
Objectives: The aim of our research was to adapt the adolescent version of The Parental Reflective Functioning Questionnaire to the Hungarian language. Methods: In our study 240 mothers completed the adolescent version of The Parental Reflective Functioning Questionnaire (PRFQ-A), and the Reflective Function Questionnaire (RFQ). Results: Confirmatory factor analysis did not confirm the original three-factor structure. The principal component analysis resulted in a two-factor structure. Factors corresponded to the original questionnaire's certainty in mental states (Alpha = .81) and interest and curiosity subscales (Alpha = .70). When analyzing the relationship between parental reflective function and reflective function, the subscales of the parental reflective function questionnaire were examined with two types of median coding in addition to polar coding. During the first median coding, the frequency of scores in the middle of the scales reflected optimal mentalization, while the frequency of extreme values on the scales corresponded to less favorable reflective functioning. With the second median coding, hypermentalization and hypomentalization subscales were also created. The second median transcoding proved to be the most suitable for capturing the relationship between RFQ and PRFQ-A. Conclusions: The questionnaire proved to be a reliable measure on the Hungarian sample and we recommend using the additional subscales.

EPP0249
Clinical characteristics of lurasidone-treated patients in Spain using Natural Language Processing -A realworld data study with Electronic Health Records. Introduction: Schizophrenia is a chronic neuropsychiatric disorder which affects over 20 million people worldwide. Atypical antipsychotics are the first-line choice for the treatment of schizophrenia due to improved tolerability and diminished risk of extrapyramidal symptoms. Lurasidone is an atypical antipsychotic approved in Spain for the treatment of schizophrenia in September 2019. An RWD-based picture of lurasidone use is necessary to better understand its impact in routine clinical practice. Objectives: To set up a methodology based on Natural Language Processing (NLP) and machine learning for the analysis of the freetext information contained in the EHRs of patients treated with lurasidone in Spain. Methods: A multicenter, retrospective study based on RWD collected in EHRs of lurasidone users will be conducted in hospitals from the Spanish National Healthcare System. Information extracted from the free text in EHRs using NLP will be treated and analyzed as big data. Results: A study database for lurasidone-treated patients in Spain has been instituted using the EHRead® technology (Figure 1), which applies machine learning and deep learning to extract, analyze, and interpret the free-text information written in their de-identified EHRs. Sociodemographic and clinical variables in EHRs from September 2019 until the most recent data available are being collected to describe the target patient population and address treatment-related outcomes.
Conclusions: NLP of free text in EHRs of lurasidone-treated patients renders a real-world picture of lurasidone usage in Spain. Studies using artificial intelligence techniques represent a novel source of information regarding psychiatric disorders and their clinical management. Introduction: Mental healthcare provision is undergoing substantial reconfiguration in many regions of the world. Such changes require a broad evidence-based approach incorporating epidemiological data and information of local needs. Objectives: To estimate the prevalence of schizophrenia spectrum disorders (SSDs) in the Lazio region and its geographical distribution using the regional health information systems (HIS). Methods: Cases of SSDs (15-64-year-old) were identified using an algorithm based on data from the hospital discharge registry [ICD