| Literature DB >> 31581531 |
Pamela J Rakhshan Rouhakhtar1, Steven C Pitts1, Jason Schiffman2.
Abstract
Self-report tools of psychosis-like experiences contribute to the understanding of psychosis and may aid in identification and prevention efforts across the severity spectrum. Current tools are likely limited by biases, leading to potential systematic health disparities. Principal component analyses in diverse samples of community participants reporting psychosis-like experiences may aid in the detection of measurement biases. The current study evaluated the fit of a two-component model for the Prime Screen, a self-report psychosis-like experiences measure, in a sample of Black (n = 82) and White (n = 162) community participants, and subsequently evaluated the relation of these components with measures of mental well-being, traumatic life experiences, community violence, and experiences of discrimination. Analyses indicated limited support for a two-component model of the Prime Screen, with four of the items showing high cross-loading across both components ("poor fit" items). Although many Prime Screen items correlated with mental well-being as expected, correlations between item scores and mental well-being were non-significant for poor fit items. Community violence emerged as a significant predictor of some individual item scores for both good and poor fit items, while discrimination predicted only some poor fit item scores. Results highlight the potential limitations of current self-report tools of psychosis-like experiences, as well as possible considerations for improvement for use in diverse populations.Entities:
Keywords: community violence; discrimination; measurement validity; principal components analysis; psychosis-like experiences; race; trauma
Year: 2019 PMID: 31581531 PMCID: PMC6832877 DOI: 10.3390/jcm8101573
Source DB: PubMed Journal: J Clin Med ISSN: 2077-0383 Impact factor: 4.241
Descriptive Statistics and Frequencies for Study Variables.
| Total Sample | Black Participants | White Participants | |
|---|---|---|---|
| Frequency (%)/Mean (SD) | |||
|
| |||
| Female | 191 (78%) | 64 (78%) | 127 (78%) |
| Male | 52 (21%) | 18 (22%) | 34 (21%) |
| Non-binary/third gender | 1 (<1%) | 0 (0%) | 1 (1%) |
|
| |||
| Low-risk | 149 (61%) | 46 (56%) | 103 (64%) |
| At-risk | 95 (39%) | 36 (44%) | 59 (36%) |
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| |||
| Total Score | 0.95 (1.55) | 1.81 (1.90) | 0.54 (1.15) |
| 0 domains of discrimination | 127 (52%) | 22 (27%) | 105 (65%) |
| 1–3 domains of discrimination | 69 (28%) | 34 (17%) | 35 (21%) |
| 4–6 domains of discrimination | 15 (6%) | 11 (13%) | 4 (2%) |
| 7+ domains of discrimination | 3 (1%) | 2 (2%) | 1 (1%) |
| Missing/Prefer not to respond | 30 (12%) | 13 (16%) | 17 (11%) |
|
| 1.45 (1.45) | 1.39 (0.40) | 1.48 (0.43) |
|
| 21.99 (5.07) | 21.11 (3.05) | 22.44 (5.79) |
|
| 16.45 (14.07) | 16.65 (14.47) | 16.36 (13.91) |
|
| 2.34 (1.97) | 2.12 (1.94) | 2.45 (1.99) |
Correlation coefficients, descriptive statistics, and normality estimates for Prime Screen items.
Component Loadings for Principal Components Analysis of Prime Screen Items.
| Black/African American | White/Caucasian | |||
|---|---|---|---|---|
| 1 | 2 | 1 | 2 | |
|
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| −0.12 |
| 0.14 |
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|
| 0.35 |
| 0.26 |
|
|
| 0.00 |
| 0.18 |
|
|
| −0.05 |
| 0.02 |
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|
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|
|
| 7. |
|
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| −0.01 |
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| 9. I think I might feel like my mind is “playing tricks” on me |
| −0.24 |
| −0.29 |
|
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| 0.04 |
| 0.01 |
|
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| 0.06 |
| 0.06 |
|
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| 0.29 |
| −0.24 |
| % of variance | 41.63% | 12.54% | 41.67% | 12.19% |
Note: All Prime Screen items were normalized via a natural log transformation before principal components analyses were estimated. Note: Items with high loadings (≥ 0.5) on one component and non-significant loadings (≤ 0.32) on another are formatted in bold above. Those with significant cross-loadings across both components or without strong loadings on either component are formatted in italics and underlined above. Note: The percentage of variance accounted for by each component above is post-rotation.
Correlation coefficients for Prime Screen items and Mental Well-Being Scores.
| Prime Items | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 |
| 3 | 4 | 5 |
|
|
| 9 | 10 | 11 | 12 | |
| Mental Well-Being | ||||||||||||
| Black | −0.17 |
| −0.20 | −0.06 | −0.13 |
|
|
| −0.15 | −0.17 | −0.10 | −0.23 * |
| White | −0.22 ** |
| −0.19 * | −0.17 * | −0.22 ** |
|
|
| −0.33 ** | −0.10 | −0.09 | −0.32 ** |
Note: * p < 0.05, ** p < 0.01. Note: Component 2 items (“poor fit”) are italicized above.
Multivariate Linear Regression Models Predicting Good Fit Prime Screen Item Scores.
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| Item 1 | 0.08 | −0.01 | 0.00 | 0.04 |
| Item 3 | −0.01 | −0.10 | 0.33 | 0.07 |
| Item 4 | 0.01 | 0.03 | 0.40 | 0.06 |
| Item 5 | −0.04 | −0.03 | 0.19 | 0.02 |
| Item 9 | 0.03 | −0.04 | 0.19 | 0.02 |
| Item 10 | 0.00 | −0.08 | 0.39 | 0.07 |
| Item 11 | 0.09 | −0.08 | 0.10 | 0.06 |
| Item 12 | 0.02 | −0.04 | 0.09 | 0.02 |
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| Item 1 | 0.01 | 0.02 |
| 0.08 |
| Item 3 | 0.02 | 0.08 | 0.13 | 0.04 |
| Item 4 | 0.03 | −0.04 |
| 0.15 |
| Item 5 | −0.03 | 0.02 |
| 0.03 |
| Item 9 | 0.02 | −0.02 |
| 0.16 |
| Item 10 | 0.00 | −0.08 |
| 0.13 |
| Item 11 | 0.02 | 0.01 |
| 0.04 |
| Item 12 | 0.03 | −0.03 |
| 0.10 |
†p < 0.10; ** p < 0.01; *** p < 0.001.
Multivariate Linear Regression Models predicting Poor Fit Prime Screen Item Scores.
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| Item 2 | −0.00 | 0.12 * | −0.07 | 0.10 |
| Item 6 | 0.01 | −0.07 | 0.06 | 0.04 |
| Item 7 | 0.01 | −0.10 * | 0.39 | 0.10 |
| Item 8 | −0.00 | −0.04 | 0.28 | 0.03 |
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| Item 2 | 0.02 | 0.02 |
| 0.04 |
| Item 6 | −0.01 |
|
| 0.06 |
| Item 7 | 0.04 |
|
| 0.09 |
| Item 8 | 0.03 |
| −0.05 | 0.05 |
†p < 0.10; * p < 0.05.