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Record W7115687073 · doi:10.1007/978-3-031-95659-1_20

Retention and Attraction Measures in the Quebec Health Care System During the Covid-19 Pandemic

2025· book-chapter· en· W7115687073 on OpenAlexaffabout

Bibliographic record

VenueSpringer proceedings in mathematics & statistics · 2025
Typebook-chapter
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsOutrageGovernment (linguistics)PandemicHealth careIncentivePublic health

Abstract

fetched live from OpenAlex

Abstract The Covid-19 pandemic has led to many changes in the management of personnel within the Quebec health care system. Faced with the increasing burden of labor needs and the need to motivate caregivers to stay in the system, the Quebec government had to put in place various measures. Some of these measures were aimed at attracting new workers and others were aimed at retaining those already involved in the health care system. The arrival of these incentives was generally well received, but their gradual withdrawal was met with outrage by health care workers’ rights groups. Our study aims to describe the measures that were introduced between March 2020 and January 2023 by the government to maintain the health care system. The study also presents the reactions that these measures generated from advocacy groups.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.376
Teacher spread0.303 · 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 designQualitative
Domainnot available
GenreOther

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
Published2025
Admission routes2
Has abstractyes

Explore more

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