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Record W3213246009 · doi:10.3917/gs1.pr1.0002

COVID-19 en hébergement au Québec

2021· article· fr· W3213246009 on OpenAlexaffabout
Marie Beaulieu, Julien Cadieux Genesse

Bibliographic record

VenueGérontologie et société · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPolitical scienceCoronavirus disease 2019 (COVID-19)PhilosophyMedicine

Abstract

fetched live from OpenAlex

La crise sociosanitaire causée par la COVID-19 a mis en exergue la pénurie de main-d’œuvre dans les centres d’hébergement et de soins de longue durée (CHSLD) du Québec – équivalent des Ehpad en France – et a mené à un constat de maltraitance organisationnelle. En réponse, le gouvernement a rapidement mis en œuvre diverses solutions, dont le déploiement d’une formation abrégée tentant ainsi de recruter 10 000 nouveaux préposés aux bénéficiaires (PAB) – appelés aides-soignants en France. La formation abrégée vise l’acquisition de 8 des 15 compétences courantes chez un PAB. Cet article, appuyé sur une analyse de documents gouvernementaux, journalistiques et académiques, pose un regard critique sur les bénéfices et les écueils anticipés de cette solution et propose des pistes en vue de les amoindrir. La contribution de ces PAB nouvellement formés vise à améliorer les conditions de travail des employés actuels, à favoriser une stabilisation des équipes et à rehausser la qualité des soins et services offerts aux aînés en CHSLD. Cependant, le programme abrégé ne comprend pas l’acquisition de certaines compétences relationnelles, dont celles ayant trait à la lutte contre la maltraitance.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.098
GPT teacher head0.434
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2021
Admission routes2
Has abstractyes

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