Les territoires ruraux de l’est du Quebec a l’epreuve de la Covid19. Marginalisation et exclusion sociales des personnes ainees ?
Bibliographic record
Abstract
Since the beginning of the pandemic, the elderly have been identified as being among the most \nat risk of developing complications in case of contamination by the Covid-19 virus. In Quebec, \nas in many parts of the world, specific measures have been put in place to protect them, \nfocusing on limiting physical and social contacts, with repercussions on their social and \npsychological well-being. In this article, we are particularly interested in the situation \nexperienced by seniors in rural areas and seek to highlight the socio-territorial effects of this \nhealth crisis on the elderly by identifying, on the one hand, the deleterious effects that \naccentuate their vulnerability and, on the other hand, the protective and supportive measures \nthat enable seniors to cope with this pandemic. Based on a qualitative analysis, our article \nhighlights several possible approaches to the complex relationship between crisis, aging and \nterritory, taking into consideration simultaneously the feeling of belonging, access to resources \nand services, solidarity, and the participation of people in the dynamics of their living \nenvironment. While several factors, such as geographic characteristics, community and family \nsolidarity, and volunteer involvement, made the lives of seniors easier during the pandemic, \n3 \nothers made them more vulnerable. Among them, the feeling of a gap between the measures \ntaken and the reality experienced, the reinforcement of difficulties in accessing care and \nservices as well as the weakening of the operations of the territories due to the massive loss of \nvolunteers aged 70 and over, forced to limit their activities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".