Transitions in Mood Among Residents of Canadian Long-Term Care Facilities: The Effects of COVID-19 Individual Risk Factors and Regional Characteristics
Bibliographic record
Abstract
Long-term care home residents are among the most vulnerable populations due to their advanced age. Their health and well-being can be influenced by physical and psychosocial factors, the surrounding physical environment, and practice patterns that make them more susceptible to increased morbidity, disability, and mortality. \nMental health disorders are particularly common among residents of long-term care (LTC) homes affecting between 27% and 40% of all LTC residents in Canada. The COVID-19 pandemic had a substantial impact on the physical and mental health and well-being of residents of long-term care (LTC) homes. The increased vulnerability of older adults combined with essential preventive and infection control measures led to a challenging environment in these care facilities. The COVID-19 pandemic highlighted and magnified pre-existing challenges in the LTC system, emphasizing the importance of comprehensive strategies to safeguard the mental health of LTC residents. \nStudy 1 is a scoping review that investigates the effect of isolation and loneliness on the mood of long-term care (LTC) home residents, both before and during the COVID-19 pandemic. It provides an overview of existing literature to understand the effects of family and friends’ visits or loneliness and of COVID-19 restrictions on residents’ mood. The review shows a diversity of findings highlighting the complexity of factors influencing residents' mood during a global health crisis such as that of COVID-19. It suggests a need for a nuanced understanding of the interplay between social interactions, pandemic-induced restrictions, and individual coping mechanisms. It also highlights the need to use a standardized measure for depressive symptoms globally to prevent biases and inconsistencies that might arise from research based on different measures. \nStudy 2 is a longitudinal study evaluating the effect of COVID-19 pandemic on incident mood disturbance among Canadian long-term care home residents. It also examines the effects of COVID-19 in stratified models using resident and facility-level variables. This study shows that a variety of factors contributed to an increase in mental health challenges during the initial waves of the pandemic including, but not limited to, the potential effects of lockdown procedures. Our findings highlight the importance of implementing broad-based multidimensional interventions to ensure the mental well-being of all individuals during outbreaks. \nStudy 3 is a pan-Canadian retrospective longitudinal analysis of residents in long-term care homes. It examines the complex transition between the different mood states and absorbing states out of LTC settings using a one-step multistate Markov transition analysis. Study 2 reports incident mood disturbance among Canadian long-term care home residents; however, it does not address the multidirectional changes or the absorbing states that act as competing risks. This study can inform decisions on programs that can enhance the mood of long-term care residents by examining predictors of worsening or improving mood as well as factors predicting transition to the absorbing states. \nStudy 4 expands on our knowledge from study 3 by treating COVID-19 as a covariate to examine the effects of COVID-19 on transitions between the transient mood states and the absorbing states in comparison to the pre-pandemic period. A one-step multistate Markov transition analysis was used in a pan-Canadian retrospective longitudinal analysis. The findings suggest further knowledge on the effects of COVID-19 on mood and inform decisions on the effective programs that can improve mood during periods of outbreaks. \nThe importance of this thesis lies in its comprehensive examination of the multifaceted complex interplay between social interactions, pandemic-related measures, as well as individual and facility-level variables pre-pandemic and during the COVID-19 pandemic. The included studies provide important insights for developing targeted interventions to support positive mood of LTC residents. In conclusion, this thesis not only advances our understanding of the mental health implications for LTC residents but also informs the development of evidence-based strategies to mitigate the adverse effects of isolation and pandemic-related stressors on this vulnerable population.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".