Trajectories of psychological distress during the COVID‐19 pandemic among community‐dwelling older adults in Quebec: A longitudinal study
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic and its associated public health measures may increase the risk for psychological distress among vulnerable older adults. This longitudinal study aimed to identify predictors of psychological distress trajectories among community-dwelling older adults in Quebec, Canada. METHODS: The study spanned four time points across 13 months and three waves of the COVID-19 pandemic. The sample included 645 community-dwelling older adults ages 60 years and older in Quebec. Participants completed telephone-based interviews that included the Kessler 6-item Psychological Distress Scale (K6) to assess psychological distress at each time point as well as information on socioeconomic, medical, psychological and COVID-19 related factors. Group-based trajectory modelling was used to identify distinct trajectories of psychological distress across time. RESULTS: Three group-based trajectories of psychological distress were identified: the resilient (50.5%), reactive (34.9%), and elevated distress groups (14.6%). Individuals with mobility issues, insomnia symptoms, COVID-19 related acute stress, general health anxiety, increased loneliness symptoms, and those unable to use technology to see others were more likely to be in the reactive and elevated groups than the resilient group. Those with past mental health problems had uniquely increased odds of being in the reactive group compared to the resilient group. Individuals living in poverty and those who reported taking psychotropic medication had increased odds of being in the elevated distress group compared to the resilient group. CONCLUSION: These findings characterized distinct trajectories of psychological distress in older adults and identified risk factors for elevated distress levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".