The depth of stories: How Black young adults' disclosure of high arousal negative affect in narratives about the COVID‐19 pandemic and the BLM protests improved adjustment over the year 2020
Bibliographic record
Abstract
The present study investigates whether the way Black young adults constructed their narratives regarding the stressful events of the COVID-19 pandemic and the black lives matter (BLM) protests related to adjustment over time. A two-wave mixed prospective and retrospective longitudinal study was conducted in July and December 2020 and included a total of 90 Black young adults. Narrative reports were collected at baseline to determine the psychological interpretations of the two events and were coded based on affect disclosure. Both time points examined adjustment to the COVID-19 pandemic and the BLM protests as well as the extent to which the basic psychological needs for autonomy, relatedness, and competence were affected. Our results showed that disclosure of high arousal negative affect in narratives at baseline was associated with better adjustment over time. Additionally, results of process analyses showed that satisfaction of the basic psychological need for autonomy (e.g., feelings of personal agency, choice, and volition) mediated the association between narratives and adjustment. These results suggest that engaging in disclosure of high arousal negative affect may be associated with heightening adjustment because it enhances individuals' autonomy, perhaps resulting in a beneficial integration of the events into their broader life narratives. These findings highlight the potential of well-constructed narratives to impact adjustment over time and have implications for clinical practice to support Racialized communities during unprecedented events.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".