Cytotechnologists

Healthcare Practitioners and Technical Occupations
Código O*NET-SOC
29-2011.02

Stain, mount, and study cells to detect evidence of cancer, hormonal abnormalities, and other pathological conditions following established standards and practices.

Os nomes das ocupações e as descrições de tarefas são exibidos em inglês, tal como publicados. As etiquetas, incluindo os tipos de tarefa, estão traduzidas.

Exposição à IA

  • Fonte dos dados: ILOPublicado: 2025

    0.31

    0.09Intervalo dos valores aqui apresentados0.70

    Valor por grupo ocupacional

    Escala, base e fonte

    Índice de exposição à IA generativa, 0–1 tal como publicado

    Publicado por grupo de base ISCO-08. Ligado a esta ocupação, total ou parcialmente, aplicando tal como publicadas as tabelas de correspondência do U.S. Bureau of Labor Statistics (ISCO-08 para SOC 2010, SOC 2010 para SOC 2018)

    Conjunto de dados de origem (transferência PDF)

  • Fonte dos dados: OpenAIPublicado: 2023

    0.160

    0.000Intervalo dos valores aqui apresentados0.844
    Escala, base e fonte

    β de avaliadores humanos, 0–1 tal como publicado

    Mapeado sobre tarefas O*NET

    Conjunto de dados de origem (transferência CSV)

Exposição à IA (rubrica da OpenAI)

13 tarefas avaliadas · 9 tarefas com β ≥ 0,5 (69.2%)

β = exposição direta (E1) + 0,5 × exposição com ferramentas disponíveis (E2), segundo a definição do repositório de origem.

  • Unidade de origem: tarefas do O*NET 27.2 → código de ocupação do O*NET 31.0
  • Todas as tarefas pontuadas constam da lista de tarefas do O*NET 31.0.
Fonte
OpenAI "GPTs are GPTs" exposure rubric
Versão
gh-main-0471612
Licença
MIT License, Copyright (c) 2024 OpenAI

Tarefas

Descrições de tarefas da O*NET® 31.0 Database, com as tarefas principais primeiro.

TarefaTipoβ (OpenAI)
Examine cell samples to detect abnormalities in the color, shape, or size of cellular components and patterns.Principal0,5
Examine specimens, using microscopes, to evaluate specimen quality.Principal0,5
Prepare and analyze samples, such as Papanicolaou (PAP) smear body fluids and fine needle aspirations (FNAs), to detect abnormal conditions.Principal0,5
Provide patient clinical data or microscopic findings to assist pathologists in the preparation of pathology reports.Principal0,5
Assist pathologists or other physicians to collect cell samples by fine needle aspiration (FNA) biopsy or other method.Principal0
Document specimens by verifying patients' and specimens' information.Principal1
Maintain effective laboratory operations by adhering to standards of specimen collection, preparation, or laboratory safety.Principal0
Prepare cell samples by applying special staining techniques, such as chromosomal staining, to differentiate cells or cell components.Principal0
Submit slides with abnormal cell structures to pathologists for further examination.Principal0,5
Adjust, maintain, or repair laboratory equipment, such as microscopes.Principal0
Assign tasks or coordinate task assignments to ensure adequate performance of laboratory activities.Principal0,5
Attend continuing education programs that address laboratory issues.Principal1
Examine specimens to detect abnormal hormone conditions.Complementar0,5

Informação ocupacional

Fontes e atribuição

This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.

Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.

O*NET OnLine: 29-2011.02 Cytotechnologists

KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.

Atribuição e licenças completas