Аbstract. As the COVID-19 pandemic progresses, the observed increase in mental health issues requires more and more clinical attention. Mental disorders have become a major cause for disturbances in social adjustment, primarily due to disorders that fall into three clusters: prolonged fatigue (asthenia) with cognitive impairment; anxiety disorders with sleep disorders; and depression. The last two are also found in individuals who have not contracted SARS-CoV-2; they are seen as a result of their exposure to the stress of the pandemic. Therefore, to successfully manage the consequences of the pandemic, it is necessary to develop a cohesive clinical interpretation of mental disorders related to COVID-19 infection. Our proposed model would encompass all the above manifestations as follows: а) for the general population – by the triad of ‘nosogenic reactions’ with excessive (hyper-), normal (normo-) or ignoring (hyponosognostic) psychological responses to stress related to the semantics and individual significance of the SARS-CoV-2 diagnosis (nosos); b) for long COVID – by the biopsychosocial model as a typical combination of neurotoxic asthenia with cognitive impairment (Bonhoeffer’s neurobiological factor) that exacerbates ‘nosogenic’ anxiety and sleep disorders (psychological factor) and thus provokes a depressive response (as a social maladaptive factor)

Modern views of schizophrenia that have evolved from E.Bleuler's and K.Schneider's concepts, along with continuous empirical explorations of the nature of the disorder (mental discordance (confusion mentale) by F. Chaslin, primary defi cit of mental activity and hypotonia of consciousness (J. Berze, 1914), alogical thought disorder (K. Kleist, 1934), etc.)

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ÐåôåðàòÌåòà. Äîñë³äèòè ìîaeëèâ³ çì³íè ó ïàòåðíàõ ñòîñóíê³â íåïñèõîòè÷íèõ ïñèõ³àòðè÷íèõ ïàö³ºíò³â, ùî â³äáóâàþòüñÿ ó ïðîöåñ³ êîðîòêîôîêóñíî¿ ïñèõîäèíàì³÷íî¿ ñòà-ö³îíàðíî¿ ïñèõîòåðàﳿ, à òàêîae ¿õí³ êîðåëÿö³¿ ç êë³í³÷íèì òà ñóá'ºêòèâíèì ïîêðàùåííÿì ñòàíó ïàö³ºíò³â; âèïðîáóâàòè âàë³äí³ñòü óêðà¿íñüêî¿ âåðñ³¿ ìåòîäó öåíò-ðàëüíî¿ òåìè êîíôë³êòíèõ ñòîñóíê³â (ÖÒÊÑ). Ìàòåð³àë ³ ìåòîäè. Ó äîñë³äaeåíí³ âçÿëè ó÷àñòü ïàö³ºíòè ñòàö³îíàðó ¹2 Ëüâ³âñüêîãî îáëàñíîãî êë³í³÷íîãî ïñèõî-íåâðîëî´³÷íîãî äèñïàíñåðó -êë³í³÷íî¿ áàçè êàôåäðè ïñè-õ³àò𳿠òà ïñèõîòåðàﳿ Ëüâ³âñüêîãî íàö³îíàëüíîãî ìå-äè÷íîãî óí³âåðñèòåòó ³ì. Äàíèëà Ãàëèöüêîãî.  óñ³õ ïà-ö³ºíò³â áóëî ä³à´íîñòîâàíî íåïñèõîòè÷í³ ïñèõ³÷í³ ðîçëàäè çà ÌÊÕ-10, ³ âñ³ì áóëî ïðèçíà÷åíî ïî 10 ñåàíñ³â ³í-äèâ³äóàëüíî¿ êîðîòêîôîêóñíî¿ ïñèõîäèíàì³÷íî¿ ïñèõîòå-ðàﳿ. Äîñë³äaeåííÿ ïðîâîäèëîñÿ çà íàòóðàë³ñòè÷íèì äèçàéíîì. Îñíîâíèé ìåòîä äîñë³äaeåííÿ -ìåòîä öåíòðàëü-íî¿ òåìè êîíôë³êòíèõ ñòîñóíê³â (ÖÒÊÑ). Ïåðåä ïî÷àòêîì ïñèõîòåðàﳿ, óñ³ ïàö³ºíòè âçÿëè ó÷àñòü ó íàï³âñòðóêòóðîâàíîìó ³íòåðâ'þ, ó ÿêîìó ðîçïîâ³ëè ïðî äåñÿòü êîíêðåòíèõ âèïàäê³â (åï³çîä³â) ñòîñóíê³â ç îäí³ºþ âàaeëèâîþ äëÿ íèõ îñîáîþ íà âëàñíèé âèá³ð. Êîaeåí åï³çîä ñòîñóíê³â ïîâèíåí áóâ ì³ñòèòè òðè êîìïîíåíòè: áàaeàííÿ ïàö³ºíòà ñòîñîâíî ³íøî¿ ëþäèíè; ðåàêö³¿ ³íøî¿ îñîáè íà ö³ áàaeàííÿ; âëàñí³ ðåàêö³¿ ïàö³ºíòà ó â³äïîâ³äü. Íàé-âàaeëèâ³ø³ ïîºäíàííÿ öèõ êîìïîíåíò³â ñêëàäàëè öåíò-ðàëüí³ òåìè êîíôë³êòíèõ ñòîñóíê³â ïàö³ºíò³â. Êîìïîíåíòè áóëî âèä³ëåíî ç ðîçïîâ³äåé ç åï³çîäàìè ñòîñóíê³â òà ïåðåêëàäåíî ó ñòàíäàðòí³ êàòå´î𳿠ÖÒÊÑ ñèñòåìè êàòå´îð³é ÖÒÊÑ-ËÓ (ëåéïöèçüêî-óëüìñüêî¿). Ïåðåä ïî-÷àòêîì ë³êóâàííÿ, ïàö³ºíòè òàêîae çàïîâíþâàëè ïñèõî-ïàòîëî´³÷íèé îïèòóâàëüíèê SCL-90 ç ìåòîþ îö³íêè íà-ÿâíî¿ ó íèõ ïñèõîïàòîëî´³÷íî¿ ñèìïòîìàòèêè. Ïàö³ºíòè, ÿê³ çàâåðøèëè êóðñ ïñèõîòåðàﳿ, ïîâòîðíî ïðîõîäèëè ³íòåðâ'þ, ïðèñâÿ÷åíå ¿õí³ì ñòîñóíêàõ ³ç ò³ºþ ae âàaeëèâîþ äëÿ íèõ îñîáîþ, çàïîâíþâàëè Îïèòóâàëüíèê çì³í ó ïåðåaeèâàííÿõ òà ïîâåä³íö³ VEV-test, à òàêîae ïîâòîðíî çàïîâíþâàëè îïèòóâàëüíèê SCL-90. Ñòàòèñòè÷íà îáðîáêà ðåçóëüòàò³â ïðîâîäèëàñÿ ç âèêîðèñòàííÿì ïðî´ðàì SPSS, âåðñ³ÿ 23.0, òà Microsoft Excel. Ðåçóëüòàòè é îáãîâîðåííÿ. ²ç çàãàëüíî¿ ê³ëüêîñò³ îáñòåaeåíèõ ïàö³ºíò³â (N=51), 30 çàâåðøèëè êóðñ ïñèõîòåðàﳿ ç 10 ñåàíñ³â, à 21 -ç ð³çíèõ ïðè÷èí ïåðåðâàëè ë³êóâàííÿ. Ïåðåä ïî÷àòêîì ë³êóâàííÿ, ÷àñòêà ãàðìîí³éíèõ ðåàêö³é ³íøèõ îñ³á â åï³çîäàõ ñòîñóíê³â ñòàíîâèëà 27.52%, à ÷àñòêà äèñãàðìîí³éíèõ -72.48%; ÷àñòêà ãàðìîí³éíèõ âëàñíèõ ðåàêö³é ïàö³ºíò³â ñòàíîâèëà 23.49%, à äèñãàð-ìîí³éíèõ -76.51%. ϳñëÿ çàâåðøåííÿ ë³êóâàííÿ, ÷àñòêà ãàðìîí³éíèõ ðåàêö³é ³íøèõ îñ³á çðîñëà äî 40.26%, à ÷àñò-Ç̲ÍÈ Ó ÏÀÒÅÐÍÀÕ ÑÒÎÑÓÍʲ ÏÀÖ²ªÍҲ ϲÑËß ÊÎÐÎÒÊÎÔÎÊÓÑÍÎÏ ÑÈÕÎÄÈÍÀ̲×Íί ÑÒÀÖ²ÎÍÀÐÍί ÏÑÈÕÎÒÅÐÀÏ²Ë èçàê Î.Ë. Ëüâ³âñüêèé íàö³îíàëüíèé ìåäè÷íèé óí³âåðñèòåò ³ì. Äàíèëà Ãàëèöüêîãî Êàôåäðà ïñèõ³àò𳿠òà ïñèõîòåðàﳿ (çàâ. -ïðîô. Î.Î. Ô³ëüö) ÓÄÊ: 615.851:159.964.21 êà äèñãàðìîí³éíèõ -â³äïîâ³äíî çìåíøèëàñÿ äî 59.74% (p≤0.001); ÷àñòêà ãàðìîí³éíèõ âëàñíèõ ðåàêö³é ïàö³ºíò³â çðîñëà äî 43.38%, à ÷àñòêà äèñãàðìîí³éíèõ -çìåíøèëàñÿ äî 56.62% (p≤0.001). Ïîêðàùåííÿ ïàòåðí³â ñòîñóíê³â ïà-ö³ºíò³â ï³ñëÿ ï...