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Record W2743580210 · doi:10.1177/2156869317720713

Neighborhood Effects on Immigrants’ Experiences of Work-Family Conflict and Psychological Distress

2017· article· en· W2743580210 on OpenAlexaffabout
Marisa Young, Shirin Montazer

Bibliographic record

VenueSociety and Mental Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDisadvantagedStressorPsychologyMental healthContext (archaeology)DisadvantageVulnerability (computing)DistressImmigrationMultilevel modelDevelopmental psychologySocial psychologyClinical psychologyPsychiatryGeographyPolitical science

Abstract

fetched live from OpenAlex

The neighborhood context is considered a key institution of inequality influencing individuals’ exposure and psychological vulnerability to stressors in the work-family interface, including work-family conflict (WFC). However, experiences of neighborhood context, WFC, and its mental health consequences among minority populations—including foreign-born residents—remain unexplored. We address this limitation and draw on tenants of the stress process model to unpack our hypotheses. We further test whether our focal associations vary for mothers and fathers. Using multilevel data from Toronto, Canada (N = 794), we find that neighborhood disadvantage—measured at the census level—increases reports of WFC among all respondents except foreign-born fathers, who report a decrease in WFC as disadvantage increases. Despite this benefit, the WFC of foreign-born fathers in disadvantaged neighborhoods leads to greater distress compared to other respondents. Our findings highlight important gender differences by nativity status in the impact of neighborhood context on individual-level stressors and mental health.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.046
GPT teacher head0.368
Teacher spread0.322 · 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
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

Citations7
Published2017
Admission routes2
Has abstractyes

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