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Record W4414649431 · doi:10.1080/07481187.2025.2566083

Gender differences in grief and growth: An international gender-matched controlled study from Belgium, Canada, and Spain

2025· article· en· W4414649431 on OpenAlexafffundabout
Jacques Cherblanc, Isabelle Côté, Emmanuelle Zech, Manuel Fernández‐Alcántara, Sébastien Gaboury, Christiane Bergeron‐Leclerc, Camille Boever, Andrea Redondo-Armenteros, Francisco Cruz‐Quintana, María Nieves Pérez‐Marfil

Bibliographic record

VenueDeath Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversité du Québec à Chicoutimi
FundersCanadian Institutes of Health Research
KeywordsGriefComplicated griefDisenfranchised griefKinshipInterpersonal relationshipInterpersonal communication

Abstract

fetched live from OpenAlex

The grieving process exhibits significant variability influenced by cultural and gender factors. Inconsistencies in gender and cultural differences may be explained by the overrepresentation of bereaved women in the samples. This study explores gender differences in grief and post-traumatic growth (PTG) across Belgium, Canada, and Spain. Utilizing a gender-matched controlled design, the study analyzed data from 244 men and 244 women. Each man was matched with a woman based on kinship with the deceased, delay since loss, and country. Participants completed the Traumatic Grief Inventory-Self Report and Post-Traumatic Growth Inventory-Short Form. Results indicate that men reported significantly lower levels of grief symptoms and PTG compared to women, consistently across all three countries. These findings are discussed in relation to previous research suggesting that men exhibit more instrumental and avoidant grieving styles while women display more intuitive and expressive responses, and how results may be biased by self-report measures and recruitment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.364
Teacher spread0.288 · 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.

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

Citations4
Published2025
Admission routes3
Has abstractyes

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