ҚАЛАЛЫҚ АГЛОМЕРАЦИЯЛАРДАҒЫ ДЕНСАУЛЫҚ САҚТАУ РЕСУРСТАРЫН БОЛЖАУ МОДЕЛЬДЕРІ МЕН ӘДІСТЕРІ: ӘДЕБИ ШОЛУ

Өзектілігі. Қалалық агломерациялардағы халық саны өсуі денсаулық сақтау қызметіне сұраныс пен оның кадрлық, дәрілік, қаржылық және материалдық-техникалық ресурстармен қамтамасыз етілуі арасындағы өлшенетін алшақтықты тудырады. Алматы агломерациясында шамамен 3,5 миллион тұрғын шоғырланған, ал 2030 жылға қарай олардың саны 4,5 миллионға жетеді деп болжанып отыр. Қазақстан Республикасы Денсаулық сақтау министрлігінің мәліметтері бойынша 2023 жылы дәрігерлер тапшылығы 4 864 толық уақыттық лауазымды құрады, ал қосарлас жұмыс істеу коэффициенті 1,4-ке жетіп, медициналық қызметкерлердің жүйелі шамадан тыс жүктемесін көрсетті.

Мақсат. Қалалық агломерациялардағы денсаулық сақтау ресурстарына деген қажеттілікті болжаудың қазіргі модельдерін, әсіресе машинамен оқыту құралдарын талдау және олардың Алматы агломерациясының жағдайына қолдану мүмкіндігін бағалау.

Материал және әдістер. 2010 жылғы қаңтардан 2026 жылғы наурызға дейінгі кезеңде PubMed, Scopus, Web of Science Core Collection және WHO IRIS дерекқорларында әдебиет іздеу жүргізілді. Шолуға дәлдігін бағалайтын сандық болжамды модельдерді қамтитын түпнұсқа зерттеулер, жүйелі шолулар және WHO/OECD әдістемелік есептері енгізілді (MAE, RMSE, MAPE, AUROC, R²) қалалық агломерациялар немесе урбанизация деңгейі ≥ 50 % аймақтар бойынша дәлдігін бағалайтын сандық болжамды модельдерді қамтитын түпнұсқа зерттеулер, жүйелі шолулар және ДДҰ/ЭЫДҰ әдістемелік есептері қарастырылды. Анықталған 1 286 жазбаның ішінен шолуға 57 басылым енгізілді.

Нәтижелер. Статистикалық модельдер ең көп тараған (40,4 %), одан кейін бақылаумен жүргізілетін машиналық оқыту әдістері (34,0 %) және гибридті модельдер (17,0 %) келді; бақылаусыз машиналық оқыту әдістері тек 4 басылымда (8,5 %) қолданылды. Тек 3 модель (6,4 %) Қазақстандағы деректер негізінде тексерілген және тек адам ресурстары санатын қамтиды. Ешбір модель барлық төрт ресурс санатын бір уақытта қамтымайды. 

Қорытындылар. Осы шолудың методологиялық нәтижелері ұлттық жоспарлау құжаттарына сәйкес кемінде үш ресурс санатын қамтитын және болжау мерзімі 5–10 жылды құрайтын қадағаланбайтын машинамен оқытуға негізделген біріктірілген болжау моделін әзірлеуді негіздейді.

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