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Record W7001969855

Les territoires ruraux de l’est du Quebec a l’epreuve de la Covid19. Marginalisation et exclusion sociales des personnes ainees ?

2022· article· en· W7001969855 on OpenAlexaboutno aff

Bibliographic record

VenueArchivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCold Fusion and Nuclear Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingVulnerability (computing)LimitingSocial exclusionSocial vulnerabilitySocial isolationSocial issuesOlder people
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.233
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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