They don’t understand us: implications of felt understanding for psychological well-being in diverse communities
Bibliographic record
Abstract
Feeling personally understood as an individual within interpersonal contexts is linked to greater psychological well-being, but are interpersonal situations the only contexts in which people desire to feel understood by others? We theorize that individuals also experience greater psychological well-being when they feel that their social group and their group’s identity is understood by people outside their group. Furthermore, we examined whether felt understanding differs for members of marginalized (vs. dominant) groups in society. In Study 1 (nStudy1a = 236; nStudy1b = 275), two independent ethnically diverse samples revealed that people of color on average experienced lower levels of felt understanding than White people. Additionally, on average felt understanding was more robustly related to people of color’s well-being relative to White people’s well-being. Study 2 (n = 254) extended these results by examining LGBTQ2S+ community members’ experiences longitudinally across Pride month using a three-wave panel design. We found that people’s average levels of felt understanding over the three assessment periods were significantly related to their psychological well-being. Taken together, our work reveals both similarities and potential differences in how members of dominant and marginalized groups experience and are impacted by a sense of felt understanding within intergroup contexts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".