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Record W4406520240 · doi:10.31219/osf.io/9nmcb

Disparities in the mental health of children from same-sex vs opposite-sex couples in Canada

2025· preprint· en· W4406520240 on OpenAlexaboutno aff
Peiya Cao, Yihong Bai, Kristine Ienciu, Antony Chum

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyDemographyDevelopmental psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

ObjectiveDisparities in mental health outcomes between children of same-sex and opposite-sex parents remain underexplored in population-based studies. This study examines the risk of mental health-related acute care among these groups.MethodUsing Canadian Census data linked to longitudinal health records, we analyzed children aged 10–24 from 2016 to 2019. Coarsened exact matching ensured comparable groups, and survival analysis models assessed hazard ratios for mental health-related emergency department (ED) visits, with subgroup analyses by child’s sex, age group, rurality, and parental gender composition (i.e. male vs female same-sex parents).ResultsChildren of same-sex parents had a 1.52 times higher risk of mental health-related ED visits (95% CI: 1.05–2.20) compared to those of opposite-sex parents. Elevated risks were observed for male children, younger children aged 10–14, and urban residents. Children of female same-sex parents faced higher risks for anxiety and mood disorders compared to those of opposite-sex parents, while no significant differences were observed for children of male same-sex parents.ConclusionChildren of same-sex parents are at higher risk of mental health issues, likely linked to stigma, highlighting the need for targeted interventions and further research.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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