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Record W7162512182 · doi:10.7202/1124785ar

Comprendre la négociation de la charge mentale des parents en situation d’emploi dans une perspective écosystémique

2025· article· fr· W7162512182 on OpenAlexvenueno aff
Carolanne Dionne, Caroline Ouellet

Bibliographic record

VenueCanadian social work review · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Gender identityFamily lifePerspective (graphical)Social environment

Abstract

fetched live from OpenAlex

Dans le discours scientifique et les ouvrages destinés aux parents, la charge mentale, surnommée le « second quart de travail », est souvent considérée comme une problématique individuelle. S’agit-il uniquement d’une problématique individuelle ou est-ce plutôt le résultat d’un contexte complexe et écosystémique entre différents systèmes? Cette analyse critique examine l’ensemble des facteurs contribuant à la charge mentale des mères de jeunes enfants en emploi, en tenant compte des influences externes au contexte conjugal qui modulent la répartition des responsabilités entre les deux parents. En s’appuyant sur la littérature scientifique et grise, les enjeux , contraintes , exigences et pressions associés à la charge mentale rencontrés par les mères sont examinés, ainsi que leurs répercussions sur la dynamique familiale et conjugale. En conclusion, cette analyse invite à repenser les politiques familiales et les mesures en milieu de travail afin de mieux reconnaître la charge mentale des mères et d’encourager une répartition plus équitable des responsabilités parentales.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.328
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0050.009
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.325
Teacher spread0.305 · 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 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
Published2025
Admission routes1
Has abstractyes

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