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Proactive Responding to Anticipated Discrimination Based on Chronic Illness: Double-Edged Sword?

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Abstract

Purpose

Proactive strategies for avoiding stigmatization may prevent work-related discrimination (Singletary and Hebl J Appl Psychol 94:797–805, 2009), yet these strategies may also cause strain in the stigmatized. We tested a model in which previous workplace discrimination experiences and anticipated future workplace discrimination related to proactive responses (compensatory behaviors and concealing behaviors), which, in turn, related to job tension.

Design/Methodology/Approach

Survey data were obtained from 332 workers with chronic illnesses. Structural equation modeling was used to test the proposed relationships.

Findings

Perceived previous discrimination directly related to anticipated future discrimination and indirectly related to compensatory and concealing behaviors. Anticipated discrimination directly related to compensatory and concealing behaviors, and indirectly related to job tension through compensatory behaviors. Compensatory behaviors were, but concealing behaviors were not, related to job tension.

Implications

Workers with chronic illness should be educated on ways to mitigate the negative effects of compensatory behaviors, including ensuring adequate opportunities to replenish resources. Organizations should provide assistance to these workers through Employee Assistance Programs or other types of job counseling. Organization leaders and supervisors have a responsibility to build an environment of acceptance for those with chronic illness in order to reduce potential discrimination.

Originality/Value

While proactive strategies are effective in reducing negative outcomes of stigmatization, little research has explored their potential downsides. We highlight the “double-edged sword” nature of compensatory behaviors. In addition, while a large proportion of U.S. workers are managing chronic illness, this population is understudied.

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Notes

  1. 1.

    Of these 26,379 cases, 5,907 had a merit resolution (22.4%). This merit resolution percentage, which has ranged from a low of 16.28% to a high of 30.48% between the years of 1997 and 2012 (U.S. Equal Employment Opportunity Commission 2012a) is similar to merit resolution percentages for cases involving other protected classes, including women, minorities, and older workers (McMahon and Shaw 2005).

  2. 2.

    Based on feedback from an anonymous reviewer, we conducted all analyses using both the summed index and a single dichotomous variable (in which anyone who reported any instance of discrimination had a value of “1” and those who did not report any instances of discrimination had a value of “0”). We did not observe any differences in the statistical significance of the hypothesized model paths when using the two different forms of the perceived previous discrimination variable.

  3. 3.

    A χ2 comparison test was not possible because a WLSMV estimator was used to handle the categorical indicators for the previous discrimination scale. Instead, a two-step process was used in which the less restrictive model was first estimated in Mplus and the derivatives needed for the χ2 test were saved. Then, the nested model was estimated and the χ2 difference test was computed in Mplus using derivatives from both models (Muthén and Muthén 1998–2011).

  4. 4.

    This measurement model test excluded the perceived previous discrimination variable, which was used as an observed variable (sum of positively endorsed items) for model testing to allow for χ2 model comparisons and use of the SRMR index. Latent variables included were: anticipated discrimination, concealing behaviors, compensatory behaviors, and job tension.

  5. 5.

    Due to concerns that model fit may be a function of the large number of correlations between control variables and study variables, we tested the structural model for fit both with and without the control variables included. The fit of the model with no control variables was also good: χ2(201) = 401.62; CFI = 0.94; RMSEA = 0.06; SRMR = 0.05.

  6. 6.

    This could possibly indicate that some workers were referencing their responses made to previous perceived discrimination experiences when answering the compensatory behaviors scale; it could also indicate that compensatory behaviors are affected both by current anticipated discrimination and previous perceived discrimination experiences. It is feasible that past experiences affect current behavior, in addition to anticipated future experiences.

  7. 7.

    We tested a model in which compensatory behaviors was removed, and in this new model, the path from concealing behaviors to job tension was statistically significant (β = 0.32, p < 0.01).

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Appendix

Appendix

Description of supplemental dataset and supplemental measures used.

Participants and Procedure for Supplemental Study

In assessing the validity of the previous perceived discrimination and anticipated discrimination scales, we used a supplemental dataset from another sample of U.S. adults with various chronic illnesses who were working at least 20 h per week (n = 193). Participants were recruited using the online site M-Turk. Researchers submit payments to the M-Turk site, and in turn participants receive payment from M-Turk to complete tasks online. In order to recruit just those participants from the U.S. who were working 20+ hours per week for an organization for pay who have one or more chronic illnesses, a screening study, which was only viewed by M-Turk workers in the U.S., was first deployed to 4,000 individuals. Only those who those qualified based on their previous responses were invited to participate in the full survey (n = 429). The prescreening questions were: “Do you work for another employing organization besides M-Turk?” (If yes) “How many hours per week do you work (on average) for your employing organization?” “Do you have a chronic or ongoing health condition?” Of the 429 individuals invited to complete the survey, 203 completed the survey and were paid a small incentive from M-Turk. In addition, four items were included to detect insufficient effort responding (e.g., “Please select “strongly agree” for your response to this question.”) Individuals who incorrectly responded to more than one of these items were removed from the dataset (n = 10). In total, 193 participants who qualified based on the prescreening survey and who did not fail more than one insufficient effort responding item were included in the dataset for analysis.

The slight majority (53 %) of participants in this M-Turk sample was male. The sample was generally well-educated: 58 % had at least a 4-year college degree. Hours worked per week ranged from 20 to 70; the average was 38.98 h (SD = 9.09). Participants’ ages ranged from 18 to 64; the average age was 33.02 (SD = 9.42). Average tenure in the current job was 4.8 years (SD = 4.66). Participants’ illnesses included anxiety disorder (n = 24), depression (n = 19), chronic pain (n = 19), diabetes (n = 15), asthma (n = 13), irritable bowel syndrome (n = 9), Crohn’s disease (n = 8), fibromyalgia (n = 8), arthritis (n = 7), migraines (n = 7), bipolar disorder (n = 5), obesity (n = 5), hypertension (n = 4), polycystic ovary syndrome (n = 4), rheumatoid arthritis (n = 4), post-traumatic stress disorder (n = 3), attention deficit disorder (n = 2), ankylosing spondylitis (n = 2), HIV/AIDS (n = 2), and ulcerative colitis (n = 2).

Scales Used When Assessing Correlations in Supplemental and Current Study Data

We used the following scales when assessing correlations for validity evidence (again, for the Anticipated Discrimination and Previous Perceived Discrimination scales). To measure procedural justice, we used five items from Niehoff and Moorman (1993) in both samples (α = 0.87 for Sample A and α = 0.92 for Sample B). To measure affective commitment, we used five items from Meyer et al. (1993) in both samples (α = 0.92 for Sample A and α = 0.89 Sample B). We measured anxiety in Sample A only using six items from the Brief Symptom Inventory (Derogatis and Spencer 1983; α = 0.88. We measured illness severity in both samples by asking participants to indicate how many medications they take on a regular basis for their chronic illness, how many medical appointments they have in a typical year for their illness, and how many emergency room visits or hospitalizations they have had for their illness in the past year. The items were each standardized; a mean composite was created.

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McGonagle, A.K., Hamblin, L.E. Proactive Responding to Anticipated Discrimination Based on Chronic Illness: Double-Edged Sword?. J Bus Psychol 29, 427–442 (2014). https://doi.org/10.1007/s10869-013-9324-7

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Keywords

  • Chronic illness
  • Compensatory behaviors
  • Discrimination
  • Proactive coping
  • Stigma