Measuring Whether Gratitude and Loneliness Mediate the Link Between Non-organizational Religiosity and Suicidal Ideation: Evidence From Black Adults During COVID-19
Bibliographic record
Abstract
Objectives: Suicides among Black adults in the United States increased nationally during the COVID-19 pandemic, although limited empirical evidence documents the pathways that explain how suicide risk may develop in this population. We examined experiences of non-organizational religious involvement, gratitude, and loneliness and their relation to suicidal ideation among Black adults in the United States. Methods: We analyzed data from a probability-based sample of 995 Black adults in the United States who completed online surveys from April through June 2022. We recruited participants from the AmeriSpeak panel at the National Opinion Research Center. We applied structural equation modeling techniques to measure direct and indirect associations among religiosity, positive psychology, and mental health variables. We tested whether non-organizational religiosity was indirectly associated with suicidal ideation via feelings of gratitude and COVID-19–specific forms of loneliness during the pandemic. Results: The measurement model demonstrated a good fit to the data. Structural model results indicated that non-organizational religious involvement was positively related to gratitude (β = 0.51; P < .001); in turn, feelings of gratitude were associated with reduced suicidal ideation (β = −0.12; P = .02). Moreover, COVID-19–specific forms of loneliness were positively associated with past-year suicidal ideation (β = 0.11; P = .01). Non-organizational religious involvement, however, was not directly associated with feelings of COVID-19–related loneliness or suicidal ideation. Conclusions: Public health officials should account for feelings of gratitude and loneliness as mechanisms that can be leveraged to inform the development of evidence-based suicide prevention interventions for Black adults during public health emergencies such as the COVID-19 pandemic and beyond.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".