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Enregistrement W221290268

Where Vocational Rehabilitation Consumers Work According to the Standard Occupational Classification System

2010· article· en· W221290268 sur OpenAlexaboutno aff
Daniel L. Boutin

Notice bibliographique

RevueJournal of rehabilitation · 2010
Typearticle
Langueen
DomaineHealth Professions
ThématiqueOccupational Health and Safety Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCensusPopulationGovernment (linguistics)Fiscal yearVocational educationDocumentationPsychologyPolitical scienceDemographySociologyLawComputer sciencePedagogy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

By incorporating the Standard Occupational Classification (SOC) into case service documentation with the start of the 2007 fiscal year (FY) on October 1, 2006, the Rehabilitation Service Administration's (RSA) vocational rehabilitation (VR) program joined other federal agencies in using a standardized approach to classifying occupational information (Levine & Salmon, 1999; RSA, 2006). As a result, the SOC replaces the outdated Department of Labor's Dictionary of Occupational Titles (DOT) as the formal system to classify the type of occupation achieved by VR consumers (Mariani, 1999; U.S. Department of Labor, 1991). Since the concept of employment is central to many rehabilitation practitioners and people with disabilities (Fraser, Vandergoot, Thomas, & Wagner, 2004; Martz & Xu, 2008), it is important to explore and describe this baseline occupational information for consumers of the VR program and the general United States population. Preceding this analysis is a brief history on classifying occupations followed by an overview of the SOC. Classifying Occupations The first occupational classification system in the United States was developed with the 1850 Census of Population (Levine & Salmon, 1999; Levine, Salmon, & Weinberg, 1999). The government surveyed the population for the profession, occupation, or trade of each person over 15 years of age (U.S. Census Bureau, 2009a, Schedule no. 1 section). From this one question to 23 million residents across 30 states, 322 occupations were identified including cotton-gin maker, drover, lath maker, rag collector, and stevedore (Levine et al., U.S. Census Bureau, 2009c). Eventually, the census surveys included not just one, but multiple questions regarding the nature of work performed (U.S. Census Bureau, 2009b). The U.S. government has since been collecting occupational data on its residents for almost 160 years. Following the rapid expansion of U.S. manufacturing in the early decades of the 20th century, a Standard Industrial Classification (SIC) system was created to systematically organize expanding industries (Levine et al.). An important aspect of the SIC was that it conformed to the existing industrial structure of the United States (Pearce, 2009). For example, industries were classified by a four-digit code and represented agriculture, communication, construction, electric, finance, fisheries, forestry, gas, insurance, manufacturing, mining, real estate, retail, sanitary services, services, transportation, and wholesale trade. The most recent version of the SIC was hierarchically sectioned into divisions (e.g., Division I: Services), major groups (e.g., Major Group 83: Social Services), and industrial groups (e.g., Industrial Group 8331 : Job Training and Vocational Rehabilitation Services) (U.S. Department of Labor, 2009). The SIC was used for many decades until it began to show signs of stress as the nation shifted to a service-oriented economy with the introduction of new industries such as information technology, healthcare, and high-tech manufacturing (Levine et al., 1999). In addition, the 1994 North American Free Trade Agreement (NAFTA) opened up trade with Canada and Mexico, signaling the need for an international industrial classification system. In 1997, the Office of Management and Budget (OMB) replaced the ailing SIC with the North American Industry Classification System (NAICS) (Murphy, 1998). The NAICS is hierarchically organized by groups of industries with similar production processes and can be individualized by participating countries to meet their own needs (Levine et al.). NAICS' six-digit classification codes allows for greater flexibility in structure than the SIC (Murphy). The highest level of aggregation represents industrial sectors (e.g., information), followed by subsectors (e.g., broadcasting and telecommunications), industry groups (e.g., radio and television), international industries (e. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,012
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,050
Tête enseignante GPT0,453
Écart entre enseignants0,403 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2010
Routes d'admission1
Résumé présentoui

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