{"id":12711,"date":"2021-07-09T19:04:12","date_gmt":"2021-07-09T10:04:12","guid":{"rendered":"https:\/\/adc-japan.com\/uk\/?p=12711"},"modified":"2026-10-18T08:00:15","modified_gmt":"2026-10-17T23:00:15","slug":"ujian-uchida-kraepelin-dan-transformasi-digital-dx","status":"publish","type":"post","link":"https:\/\/adc-japan.com\/uk\/ms\/tips-ms\/12711.html","title":{"rendered":"Ujian Uchida-Kraepelin dan Transformasi Digital (DX)"},"content":{"rendered":"<aside class=\"uk-ai-translation-notice\" lang=\"ms\" style=\"box-sizing:border-box;clear:both;margin:0 0 1.5em;padding:1em;border:1px solid #ccd5de;border-left:4px solid #486581;background:#f4f7fa;color:#243746;font-size:14px;line-height:1.8;overflow-wrap:anywhere;\">\n<p style=\"margin:0;\">Halaman ini diterjemahkan secara automatik daripada teks asal bahasa Jepun menggunakan AI generatif. Terjemahan mungkin mengandungi kesilapan. Sila hubungi kami untuk mengesahkan maklumat penting.<\/p>\n<\/aside>\n<h3>Menggunakan DX untuk mengendalikan ciri manusia<\/h3>\n<p>Ujian Uchida-Kraepelin amat sesuai untuk mengendalikan ciri keupayaan serta personaliti\/tingkah laku manusia dalam transformasi digital (DX).<\/p>\n<h3>Apakah DX?<\/h3>\n<p>Transformasi digital merujuk kepada gagasan bahawa <strong><span style=\"color: #ff6600;\">penyebaran teknologi maklumat mengubah kehidupan manusia ke arah lebih baik dalam semua aspek<\/span><\/strong>. Konsep ini lazimnya dikaitkan dengan Profesor Erik Stolterman dari Universiti Ume\u00e5, Sweden, pada 2004, dan disingkatkan sebagai DX.<\/p>\n<p>Kementerian Ekonomi, Perdagangan dan Industri Jepun menerangkan DX dalam Garis Panduan Penggalakan DX seperti berikut (parafrasa):<\/p>\n<p><span style=\"color: #ff6600;\"><strong>Perniagaan bertindak balas terhadap perubahan pesat persekitaran dengan menggunakan data dan teknologi digital untuk mengubah produk, perkhidmatan dan model perniagaan mengikut keperluan pelanggan serta masyarakat. Pada masa yang sama, operasi, organisasi, proses dan budaya korporat diubah untuk mewujudkan kelebihan daya saing.<\/strong><\/span><\/p>\n<h3>Ujian Uchida-Kraepelin dan DX<\/h3>\n<p>Oleh sebab Ujian Uchida-Kraepelin dijalankan menggunakan kertas dan pensel, ia mungkin kelihatan tidak berkaitan dengan teknologi digital.<br \/>\nNamun, kaedah unik yang hanya melibatkan tugas menambah angka serta mengendalikan data jumlah kerja dan kesilapan setiap minit ini pada asasnya bersifat digital.<br \/>\nGraf dan teks pada laporan penilaian hanyalah gambaran visual data digital supaya mudah dibaca manusia.<\/p>\n<figure class=\"uk-dx-image\" style=\"margin:0 0 24px;\"><a href=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms.png\"><img decoding=\"async\" width=\"1680\" height=\"830\" class=\"wp-image-12038 alignnone\" src=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms.png\" alt=\"Ujian Uchida-Kraepelin: mendigitalkan ciri keupayaan dan tingkah laku; Data raya\" style=\"max-width:100%;height:auto;width:600px;\" srcset=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms.png 1680w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms-300x148.png 300w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms-1024x506.png 1024w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms-768x379.png 768w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-ms-1536x759.png 1536w\" sizes=\"(max-width: 1680px) 100vw, 1680px\" \/><\/a><figcaption>\n<details>\n<summary>Ujian Uchida-Kraepelin: mendigitalkan ciri keupayaan dan tingkah laku<\/summary>\n<div class=\"uk-dx-diagram\" style=\"max-width:600px;border:1px solid #ccc;padding:16px;box-sizing:border-box;line-height:1.7;overflow-wrap:anywhere;\">\n<p style=\"background:#20478f;color:#fff;padding:12px;text-align:center;\"><strong>Ujian Uchida-Kraepelin: mendigitalkan ciri keupayaan dan tingkah laku<\/strong><br \/>\u2193<br \/>Data mentah keputusan ujian<\/p>\n<p style=\"background:#e7f0f5;padding:12px;text-align:center;\"><strong>Data raya<\/strong><br \/>&#x2194; Bandingkan dengan data klinikal<br \/>\u2193 Bangunkan dan kemas kini model penilaian<br \/>Keputusan penilaian daripada pelbagai model<\/p>\n<ul style=\"margin-bottom:0;\">\n<li>Kesesuaian kerja: penilaian pengambilan, penempatan dan pertukaran<\/li>\n<li>Pengurusan keselamatan: pencegahan kemalangan dan sistem lesen memandu<\/li>\n<li>Pendidikan dan latihan: perancangan pendidikan, kaunseling pengajian lanjut, pendidikan masyarakat dan sokongan kerjaya<\/li>\n<li>Kesihatan mental: kesejahteraan emosi, penilaian kesihatan dan sokongan kembali bekerja<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<\/figcaption><\/figure>\n<p>Ujian ini terutamanya menghasilkan dua jenis data:<\/p>\n<ol>\n<li>Data mentah ujian seperti jumlah kerja, bilangan kesilapan setiap minit dan umur<\/li>\n<li>Data keputusan penilaian apabila data mentah dimasukkan ke dalam model penilaian<\/li>\n<\/ol>\n<p>Model penilaian ialah fungsi yang diperoleh dengan membandingkan data mentah dan data klinikal, seperti cara bekerja lazim, gaya pembelajaran, kesesuaian tugas tertentu, kemalangan dan kesihatan. Model sentiasa dikemas kini supaya sesuai dengan tujuan yang berubah mengikut masa.<\/p>\n<p>Perkhidmatan penilaian awan Ujian Uchida-Kraepelin membekalkan fail CSV yang mengandungi keputusan penggunaan model tertentu pada data mentah.<\/p>\n<p>Jika model yang menepati tujuan sudah tersedia, keputusan model itu boleh digunakan. Jika belum, AI boleh menganalisis hubungan data klinikal dengan data mentah atau keputusan penilaian sedia ada untuk membangunkan model baharu.<\/p>\n<p>Pertimbangkan Ujian Uchida-Kraepelin untuk inisiatif DX anda.<\/p>\n<h3>Cadangan DX: menggunakan ujian untuk membantu mengurangkan kemalangan jalan raya<\/h3>\n<p>SDG 3 tentang kesihatan dan kesejahteraan untuk semua merangkumi sasaran 3.6 untuk mengurangkan separuh kematian serta kecederaan akibat kemalangan jalan raya. Tarikh sasaran asalnya ialah 2020. Antara pelbagai langkah keselamatan, mari pertimbangkan penggunaan Ujian Uchida-Kraepelin.<\/p>\n<p>Kegunaannya memerlukan kajian sama ada ciri keupayaan, personaliti dan tingkah laku berkorelasi dengan kemalangan. Artikel asal melaporkan taburan ciri penilaian yang berbeza antara kumpulan yang pernah menyebabkan kemalangan serius dan kumpulan yang tidak pernah menyebabkan kemalangan.<\/p>\n<p>Dengan kriteria penilaian sementara untuk membezakan kecenderungan risiko lebih rendah dan lebih tinggi, data sebenar digunakan untuk membahagikan setiap populasi kepada A dan B seperti berikut.<\/p>\n<div class=\"uk-dx-data-table\" tabindex=\"0\" role=\"region\" aria-label=\"Contoh keselamatan jalan raya: perbandingan kumpulan\" style=\"max-width:100%;overflow-x:auto;\">\n<table style=\"min-width:420px;width:100%;\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n<tbody>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; width: 52pt; text-align: center;\" width=\"69\" height=\"24\"><\/td>\n<td class=\"xl65\" style=\"border-left: none; width: 52pt; text-align: center;\" align=\"left\" width=\"69\">Kumpulan terlibat kemalangan<\/td>\n<td class=\"xl65\" style=\"border-left: none; width: 52pt; text-align: center;\" align=\"left\" width=\"69\">Kumpulan tanpa kemalangan<\/td>\n<\/tr>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; border-top: none; text-align: center;\" align=\"left\" height=\"24\">Kumpulan A<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">38%<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">76%<\/td>\n<\/tr>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; border-top: none; text-align: center;\" align=\"left\" height=\"24\">Kumpulan B<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">62%<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">24%<\/td>\n<\/tr>\n<tr>\n<td style=\"border-top: none; text-align: center;\">Jumlah<\/td>\n<td style=\"border-top: none; border-left: none; text-align: center;\">100%<\/td>\n<td style=\"border-top: none; border-left: none; text-align: center;\">100%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Pembahagian ini tidak dapat memisahkan semua orang yang mengalami kemalangan daripada yang tanpa kemalangan. Namun, dalam perbandingan ini, A mempunyai kecenderungan berkaitan kemalangan yang lebih rendah daripada B.<\/p>\n<p>Apakah perubahan kadar contoh jika B tidak memandu? Artikel Jepun tahun 2021 menggunakan andaian kebarangkalian kemalangan sekitar 1% setahun. Jika digunakan pada 10,000 orang, jadual berikut diperoleh.<\/p>\n<div class=\"uk-dx-data-table\" tabindex=\"0\" role=\"region\" aria-label=\"Contoh keselamatan jalan raya: perbandingan kumpulan\" style=\"max-width:100%;overflow-x:auto;\">\n<table style=\"min-width:420px;width:100%;\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"69\"><\/td>\n<td style=\"text-align: center;\" width=\"68\">Kumpulan terlibat kemalangan<\/td>\n<td style=\"text-align: center;\" width=\"69\">Kumpulan tanpa kemalangan<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Kumpulan A<\/td>\n<td style=\"text-align: center;\">38<\/td>\n<td style=\"text-align: center;\">7524<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Kumpulan B<\/td>\n<td style=\"text-align: center;\">62<\/td>\n<td style=\"text-align: center;\">2376<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Jumlah<\/td>\n<td style=\"text-align: center;\">100 (1%)<\/td>\n<td style=\"text-align: center;\">9900 (99%)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Pengiraan dalam artikel asal:<br \/>\nKumpulan A: 38 \u00f7 7524 \u2248 0.5%<br \/>\nKumpulan B: 62 \u00f7 2376 \u2248 2.6%<br \/>\nDalam contoh ini, angka B melebihi lima kali angka A. Jika B tidak memandu, kadar dalam kalangan pemandu yang tinggal menjadi sekitar 0.5%, berbanding andaian 1%, iaitu lebih kurang separuh. Lihat nota di bawah tentang penyebut semua ahli kumpulan.<br \/>\nNamun, tidak memberikan lesen kepada B turut menghalang 2,376 orang tanpa kemalangan daripada memandu, maka pendekatan itu tidak realistik.<br \/>\nTeknologi maklumat mungkin membantu di sini. Artikel mencadangkan agar B diberi keutamaan dalam galakan beralih kepada ASV (kenderaan keselamatan termaju) atau kenderaan automatik, dengan tujuan mencapai manfaat keselamatan yang serupa.<\/p>\n<p>Artikel asal menyifatkan Jepun mempunyai kemalangan yang agak sedikit berbanding negara lain dan mencadangkan potensi lebih besar di negara yang sistem lesen atau peraturan lalu lintasnya kurang maju. Ini cadangan artikel, bukan kesan yang terbukti di semua negara.<br \/>\nKesihatan dan kesejahteraan untuk semua.<\/p>\n<div class=\"uk-editorial-note\" style=\"border-left:4px solid #777;padding:12px;background:#f5f5f5;\"><strong>Nota tentang contoh asal dan pengiraan<\/strong><\/p>\n<p>Terjemahan ini mengekalkan jadual dan contoh artikel Jepun bertarikh 9 Julai 2021. Kadar tahunan 1% ialah andaian contoh, bukan statistik negara semasa. Tarikh sasaran asal SDG 3.6 ialah 2020. Formula asal membahagikan bilangan yang mengalami kemalangan dengan yang tanpa kemalangan; perkadaran berdasarkan semua ahli kumpulan ialah 38\/(38+7524) \u2248 0.50% dan 62\/(62+2376) \u2248 2.54%. Contoh ini bukan bukti sebab-akibat, ramalan risiko individu yang disahkan, atau jaminan pengurangan kemalangan melalui ujian, ASV atau kenderaan automatik. Penggunaan dalam populasi lain memerlukan pengesahan. Jangan tentukan pengambilan pekerja, lesen atau kelayakan lain berdasarkan ujian ini sahaja.<\/p>\n<p><a href=\"https:\/\/sdgs.un.org\/goals\/goal3\">SDG 3.6 \u2014 United Nations<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Halaman ini diterjemahkan secara automatik daripada teks asal bahasa Jepun menggunakan AI generatif. 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