Can “Rover” help with mental health during the COVID-19 pandemic? Results from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
COVID-19 has negatively affected the mental health and well-being of adults, and thus it is important to examine potential factors which may influence mental health during the pandemic. We thus examined the association between pet ownership and depression/anxiety symptoms based on mental health disorder status during the COVID-19 pandemic. We included 12,068 cognitively healthy participants (45–86 years at study entry) from the Canadian Longitudinal Study on Aging (CLSA) comprehensive cohort who completed the first follow-up ([FU1]; 2015–2018), and COVID-19 Survey entry (April–May 2020) and exit (September-December 2020). Participants self-reported at FU1 if they owned a pet (yes/no). Participants were dichotomized as with or without a mental health disorder based on self-reported diagnosis of depression, anxiety, or mood disorders at baseline assessment (2011–2015) or FU1. Depressive symptoms were indexed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10) at FU1, and COVID-19 entry/exit surveys. Anxiety symptoms were assessed using the General Anxiety Disorder Questionnaire (GAD-7) at COVID-19 entry/exit surveys. Final models adjusted for age, sex, body mass index, income, education, living status, smoking status, relationship status, and alcohol intake. Forty-percent of participants owned a pet at FU1. Among those without a mental health disorder, there were no significant differences in CESD-10 between participants who owned pets compared with those without pets. For people with a mental health disorder, pet owners had higher CESD-10 (estimated mean difference range: 0.56–1.02 points; p < 0.05) and GAD-7 scores (estimated mean difference range: 0.28–0.57 points; p < 0.05) at both COVID-19 entry and exit surveys. Among people with mental health disorders, pet ownership was associated with poor mental health symptoms during April 2020 to December 2020 of the COVID-19 pandemic.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".