MétaCan
Menu
Back to cohort
Record W4287840306 · doi:10.3917/esra.004.0007

Perceptions et réactions d’intervenants québécois face à la dispensation de services sociaux aux jeunes et aux familles en temps de COVID-19

2022· article· fr· W4287840306 on OpenAlexaffabout
Marie-Claude Simard, Ève Pouliot, Danielle Maltais, Danielle Nadeau, Delphine Collin‐Vézina, Sara Séguin-Baril, Julie Tremblay, France Nadeau

Bibliographic record

VenueÉcrire le social · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsMcGill UniversityUniversité LavalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSociologyPolitical scienceArtMedicine

Abstract

fetched live from OpenAlex

Cette étude qualitative vise à documenter les perceptions et les réactions des intervenants du réseau des services sociaux à l’enfance, jeunesse, famille face aux conséquences de la pandémie de COVID-19. Entre la 3 e et la 4 e vague de la pandémie au Québec, 11 intervenants, incluant deux gestionnaires, ont pris part à des groupes de discussion. Les résultats révèlent qu’ils ont ressenti de la peur, de la colère, de l’anxiété, des sentiments d’impuissance et d’incompétence, des tensions et ont dû faire face à une polarisation des opinions, ainsi que de la surcharge et de l’épuisement. Pour s’adapter à cette situation, ils ont employé diverses stratégies, dont la méditation et le télétravail. Ces résultats permettent de dégager cinq constats qui pourront servir à l’amélioration des pratiques et du bien-être des intervenants dans le contexte de la pandémie et en période post-pandémie.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.059
GPT teacher head0.402
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueÉcrire le socialSame topicSocial Sciences and GovernanceFrench-language works237,207