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Enregistrement W3217077791 · doi:10.1371/journal.pone.0260160

Employment outcomes and experiences of people with seeing disability in Canada: An analysis of the Canadian Survey on Disability 2017

2021· article· en· W3217077791 sur OpenAlexaffabout
Shikha Gupta, Mahadeo A. Sukhai, Walter Wittich

Notice bibliographique

RevuePLoS ONE · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDisability Education and Employment
Établissements canadiensCNIB FoundationQueen's UniversityUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésDescriptive statisticsAffect (linguistics)Logistic regressionEquity (law)LegislationGerontologyMultivariate analysisMedicinePsychologyDemographyPolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Many individuals with disabilities face barriers to meaningful employment. Legislation has been put in place to ensure employment equity for individuals with disabilities in Canada. However, little is known about the employment profile and experiences of people with seeing disabilities. OBJECTIVES: The objectives of our research study were to explore the employment rates of people with seeing disabilities in Canada, the factors associated with being employed, and supports and barriers that affect their work participation. METHODS: We used the nationally representative data from the Canadian Survey on Disability (CSD) 2017, collected by Statistics Canada. The CSD is a national cross-sectional survey of Canadians 15 years of age and above who face a functional limitation due to a health-related condition, representing more than 6 million (n = 6,246,640) Canadians. Our analyses focused on people who reported having a seeing disability. A subset of the complete dataset was created, focusing on individuals with a seeing disability. Weighted descriptive analyses were performed using SPSS. Multivariate logistic regression analyses were conducted for individuals between 25-64 years of age to identify predictors of employment. RESULTS: Out of the estimated 892,220 working-age adults (25-64 years) with a seeing disability who were represented by the survey, 54% were employed, 6% were unemployed and 40% were not in the labour force. Early onset of seeing disability (OR: 1.33; 95% CI: 1.32-1.35), less severe seeing disability (OR: 1.51; 95% CI: 1.49-1.53), education above high school (OR: 2.00; 95% CI: 1.97-2.02) and daily use of the internet (OR: 2.46; 95% CI: 2.41-2.51) were positively related with employment. The top three employment accommodations that were needed and were made available included: modified work hours (45%); work from home (38.5%) and a modified workstation (37%). The top three needed but least available accommodations were technical aids (14%), communication aids (22%) and a computer with specialized software or adaptation (27%). Overall, 26% reported that an accommodation was required but was not made available by the employer. While 75% of individuals with a seeing disability were out of the labour force due to their condition, the remaining identified barriers that prevented them from working which included (top 3): (i) too few jobs available (20%); (ii) inadequate training/experience (19%), (iii) past attempts at finding employment were unsuccessful (19%). CONCLUSION: Adults with seeing disability in Canada experience lower labour force participation than the general population. Rigorous programs are required to assist them with the job search, job retraining and workplace accommodations. It is important for governments to improve efforts towards inclusive education and develop strategies that promote digital literacy of employees and job seekers with visual impairments. Although accessibility legislations have been put in place, programs should be established that provide accessibility solutions for various employers, enabling them to hire individuals with different abilities.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,036
Score d'incertitude au seuil0,490

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,079
Tête enseignante GPT0,314
Écart entre enseignants0,235 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations37
Publié2021
Routes d'admission2
Résumé présentoui

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