For the regression predicting “being granted job changes” see Table G, column 2. The overall model including control variables had a Nagelkerke score of .205. The institutional factors were a significant but not very substantive predictor of accommodation granting,
explaining only .004 of the model fit associated with granting beyond that explained by the other predictors. Individual factors directly related to disability explained .094 of the model fit related to accommodation granting above and beyond that explained by other factors, while individual factors related to intersectional identities explained only .009. Organizational variables
explained a unique .037. Significant predictors that were positively associated with “being
granted job changes” included having a hearing or communication impairment, disability severity, age at the onset of disability, having prior experience with discrimination, being a visible minority, working full-time, tenure with the organization, and being in a scarce occupation. Significant predictors that were negatively associated with “being granted job
changes” included having memory, pain, or agility impairments, the curvilinear severity measure, age, and being a union member. The control industries of agriculture, natural
resources, trade, tourism, and personal services were all negatively associated with being granted job changes, as were all occupations (note that supervisors were the comparison category for occupations).
For the regression predicting “being granted technical interventions” see column 3 in Table G. The overall model including control variables had a Nagelkerke score of . 263.
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Institutional variables explained .007 of the model fit in accommodation granting above and beyond that explained by other factors while individual factors directly related to disability explained an additional .079, individual factors related to intersectional identities explained another .019, and organizational variables explained .015 of the total model fit. Significant predictors that were positively associated with “being granted technical interventions” included all industries except agriculture (which was non-significant), having an emotional or
developmental impairment, being a visible minority, having prior experience with
discrimination, tenure with the organization, and being a union member. Significant predictors that were negatively associated with “being granted technical interventions” included being in a professional, technical, or clerical occupation, having a pain or agility impairment, being female, age, having a permanent job, working full-time, being trained in the job, and having a scarce occupation.
For the regression predicting “being granted human support” see column 4 of Table G. As seen previously the sample size for this category is small (N=169) so results should be interpreted with caution. The overall model including control variables had a Nagelkerke score of .449. Among the 4 categories of variables both the institutional and organizational factors were not statistically significant predictors of accommodation granting. Individual factors directly related to disability explained .191 of that model fit in accommodation granting beyond that explained by other factors while individual factors related to intersectional identities
explained an additional .064. Significant predictors that were positively associated with “being granted human support” included having emotional or pain-related impairments, disability severity, being female or a visible minority, prior experience with discrimination, and working
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full-time. Significant predictors that were negatively associated with “being granted human support” included being a professional, age at onset of disability, age, and being an immigrant.
For the regression predicting “being granted structural changes” see column 5 of Table G. The overall model including control variables had a Nagelkerke score of .271. Institutional factors uniquely explained .004 of that model fit related to accommodation granting while organizational variables explained an additional .017 above and beyond other factors. Individual factors directly related to disability uniquely explained .156 of the overall model fit while
individual factors related to intersectional identities explained another .025. Significant predictors that were positively associated with “being granted structural changes” included working in the professional services industry, having hearing, seeing, communication, memory, pain, or emotional impairments, age, being a visible minority, and having prior experience with discrimination. Agility and mobility impairments were negatively associated with “being granted structural changes”, as was age at onset of the disability, being female, fulltime, trained on the job, working in a scarce occupation, and being either a professional or in a technical occupation.
For the regression predicting “being granted transportation related accommodations” see column 6 in Table C. The overall model including control variables had a Nagelkerke score of .331. Institutional factors uniquely explained .009 of the model fit related to accommodation granting while organizational variables explained an additional .01. Individual factors directly related to disability explained .191 of the model fit related to accommodation granting above and beyond that explained by other factors while individual factors related to intersectional identities explained an additional .24. Significant predictors that were positively associated with “being
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granted transportation related accommodations” included being in a trade occupation, having a hearing, seeing, memory, pain, developmental or emotional impairment, disability severity, age, and prior experience with discrimination. Significant predictors that were negatively associated with “being granted transportation related accommodations” included being a professional, having agility or mobility impairment, being female, working fulltime, and being in a scarce occupation.