Older Adult Mental Health Considerations and Differences in the COVID-19 Context: A Mixed Methods Study
Bibliographic record
Abstract
Background: \nThe COVID-19 pandemic has impacted older adult mental health in Ontario. Information around the aging and mental health considerations of older adults and their support network is lacking. There is also a knowledge gap regarding differences in older adults’ mental health since the pandemic onset. \nResearch Questions: \nThis thesis asked two research questions: \n1.\tWhat are the considerations older adults, their caregivers, and health or social care providers have regarding aging and mental health support, care, and treatment, as identified during the beginning of the COVID-19 pandemic? \n2.\tAre there differences in mental health indicators, supports, care, or treatments for older adults during the COVID-19 pandemic? \nMethods: \nA pragmatic approach was applied to a follow-up quantitative mixed methods study design involving the qualitative framework analysis of free-form survey responses (n = 268), and the quantitative analysis of first-time homecare assessments conducted in Ontario. \nResults: \nFour core areas of consideration around aging and mental health were identified: key principles that influence the experiences and outcomes of older adults; societal- and system-level factors affecting older adult mental health; valuable services, supports, and programs; and mental health experiences and outcomes as mapped to the dual-continuum model of mental health. Analysis of n = 96,919 homecare assessments indicated older adults during the pandemic had poorer mental health experiences and outcomes, even when controlling for clinical and demographic differences. \nConclusions: \n\tUnderstanding COVID-19 related older adult mental health differences and key considerations relating to aging and mental health can inform the design and application of resources for Ontarian older adults.
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".