Feelings of Social Isolation in Canada's Pandemic Times: Exploring Its Correlates Using Multiple Correspondence Analysis
Bibliographic record
Abstract
Identifying the linkages between the feelings of social isolation and its socio-demographic, residential, and psychosocial correlates is of major importance to the healthcare system today. Using data from the Canadian Survey Perspective Survey Series 6 (CPSS6) conducted by Statistics Canada, the purpose of this study is to explore these linkages during the second year of the pandemic in Canada. Five levels of social isolation were examined in conjunction with 17 other socio-demographic and psychosocial characteristics of 3,941 respondents to the survey. The study suggests that about three out of four Canadians (75%) have experienced some feeling of social isolation at some point during the pandemic. The most severe form comprised 11% of the total population. In the context of a generalized cross-tabular data analysis, Multiple Correspondence Analysis (MCA) detected two major dimensions underlying the data: mental health distress and time duration. These dimensions represented approximately 61% of the inertia or unexplained variation in the data. An examination of the combinations of variable categories revealed that the highest perceived isolation levels were found among young individuals, females, individuals who were single, those who received medical help during the pandemic, and those living in low-rise apartments in urban areas. The lowest levels of perceived social isolation were found among older individuals, rural residents, those who were married and/or reported excellent mental health. Not all individuals who experienced higher levels of mental distress, however, felt social isolation. The findings of the study could be useful when designing public health campaigns aimed at reducing social isolation by providing helpful alternatives to affected populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".