Evaluating the utility and validity of a discrimination‐specific measure of shift‐&‐persist coping
Bibliographic record
Abstract
Abstract Shift‐&‐persist (S&P) coping has been shown to buffer against the effects of discrimination on psychosocial functioning in racially and ethnically minoritized youth. However, existing measures of S&P refer broadly to coping with stress and are not specifically tailored to the type of stressor individuals are coping with (e.g., discrimination). The current study evaluated the measurement properties, utility, and validity of a discrimination‐specific adapted measure of S&P relative to an existing, general measure among a sample of 327 minoritized youth ( M age = 18.80, SD = 1.28, 78.6% female, 50.5% Black) recruited from a large public minority‐serving institution in the southeastern United States. Contrary to our hypotheses, when the item stem was changed to refer to coping specifically with discrimination, the measurement properties of a validated S&P scale (Lam et al., 2018) were worse relative to the original measure. Overall, the general S&P measure produced larger main effects and explained two times more variance in depressive symptoms than discrimination‐specific S&P. Findings do not rule out the idea that context‐specific measures may better characterize coping with discrimination experiences than ‘trait‐like’ general coping measures. However, results highlight that small adaptations to current measures may not be sufficient and may compromise predictive validity. Coping with discrimination measurement recommendations is discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".