Spirituality, Community Belonging, and Mental Health Outcomes of Indigenous Peoples during the COVID-19 Pandemic
Bibliographic record
Abstract
We aimed to assess the association between community belonging, spirituality, and mental health outcomes among Indigenous Peoples during the COVID-19 pandemic. This cross-sectional observational study used online survey distribution and targeted outreach to the local Indigenous community to collect a convenience sample between 23 April 2020 and 20 November 2020. The surveys included demographic information, self-reported symptoms of depression (PHQ-2) and anxiety (GAD-2), and measures of the sense of community belonging and the importance of spirituality. Multivariate logistic regression was used to model the association between the sense of community belonging and spirituality, and symptoms of anxiety and depression. Of the 263 self-identified Indigenous people who participated, 246 participants had complete outcome data, including 99 (40%) who reported symptoms of depression and 110 (45%) who reported symptoms of anxiety. Compared to Indigenous participants with a strong sense of community belonging, those with weak community belonging had 2.42 (95% CI: 1.12-5.24)-times greater odds of reporting symptoms of anxiety, and 4.40 (95% CI: 1.95-9.89)-times greater odds of reporting symptoms of depression. While spirituality was not associated with anxiety or depression in the adjusted models, 76% of Indigenous participants agreed that spirituality was important to them pre-pandemic, and 56% agreed that it had become more important since the pandemic began. Community belonging was associated with positive mental health outcomes. Indigenous-led cultural programs that foster community belonging may promote the mental health of Indigenous Peoples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".