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Record W7042603006

Prevalence of mental health disorders among pregnant women living in public housing in Manitoba.

2024· dissertation· en· W7042603006 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPregnancyPublic healthPsychological interventionPostpartum periodPrevalence of mental disordersMental illness
DOInot available

Abstract

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Background: Women are more likely to experience a higher burden of mental health disorders than men. Pregnancy is one of the most sensitive and complex periods in a woman’s life; the physical and psychological changes that take place during pregnancy can impact pregnant women’s mental health. In Canada, approximately one in four women experience a mental health disorder during pregnancy or the postpartum period. Maternal mental health disorders include a range of disorders and symptoms, including but not limited to depression, anxiety, and psychosis. Housing is considered one of the most critical social determinants of physical and mental health, particularly among pregnant women. Public housing (PH) is a government-funded housing program that provides affordable housing to individuals or families with limited or fixed incomes. Finlayson et al., (2013), found a higher proportion of women in PH than men. Women with mental health disorders during pregnancy and living in poor quality housing conditions are less likely to receive adequate prenatal care. They are also more likely to engage in unhealthy behaviours such as smoking and use of alcohol and other substances which are known to cause devastating consequences for mothers, babies, and society. While there is evidence to suggest that people living in PH have a higher burden of mental health disorders, little is known about these disorders among pregnant women living in PH. This is despite the unique physiological and social changes that they experience as they transition to parenthood. Objectives: The study examined the prevalence and incidence of mental health disorders (mood and anxiety, substance use, psychotic and personality disorders and any mental health disorders) among pregnant women who had a live singleton birth while living in PH and compared with pregnant women who did not live in PH during the study period. Methods: A population-based retrospective cohort study was conducted among 2,226 pregnant women who lived in PH and 57,132 pregnant women who did not live in PH, aged 18 to 45 years who experienced at least one pregnancy that resulted in a singleton, live birth between April 1, 2009–March 31, 2018 in Manitoba. Descriptive statistics were used to describe maternal demographic characteristics (age, income quintiles, receipt of income assistance, high school completion, parity, Charlson Commodity Index, region of residence). Univariate and multiple logistic regressions were used to calculate the diagnostic prevalence of mental health disorders before conception among pregnant women in both groups. Negative binomial regression was used to compare the incidence rates of these disorders among the same two groups of women during pregnancy and postpartum period. Results: When compared with the women who did not live in PH, women who lived in PH had significantly higher odds of being diagnosed with all mental health disorders five years before conception: (a) mood and anxiety disorder (OR, 2.08; 95% CI, 1.91, 2.26), (b) substance use disorder (OR, 3.03; 95% CI, 2.65-3.47), (c) personality disorder (OR, 2.85; 95% CI, 2.17-3.73), (d) psychotic disorder (OR 3.26; 95% CI, 2.25-4.72), and (e) any mental health disorders (OR 2.21; 95% CI, 2.03-2.41). However, I found no significant differences in the odds for mood & anxiety disorders, substance use disorders, and any mental health disorders when comparing women who lived in PH with their comparison after adjusting for the covariates. Personality and psychotic disorders could not be compared between the two groups in the multivariable analysis due to the small number of events. Furthermore, the unadjusted incident rates of mental health disorders diagnosed during pregnancy and after birth were significantly higher among women who lived in PH compared with their comparison for mood and anxiety disorders (RR, 1.23; 95% CI, 1.06-1.42), substance use disorders (RR, 1.54; 95% CI, 1.16-2.03), personality disorders (RR 2.71; 95% CI, 1.56-4.72), and any mental health disorders (RR 1.12; 95% CI, 0.99-1.27). There was no statistically significant difference for psychotic disorders (RR 1.30; 95% CI, 0.48-3.61). After adjusting for the covariates, the incident rate of substance use disorders was significantly lower among the women who lived in PH compared with women who did not live in PH. There were no significant differences in the incident rates for mood & anxiety disorders and any mental health disorders between the two groups. Conclusion: Unadjusted models showed that women living in PH had higher diagnostic prevalence and incidence of mental health disorders when compared with those who did not live in PH. However, in the fully adjusted models, receipt of income assistance (IA) accounted for much of the observed differences in mental health disorders between those who lived and those who did not live in PH. This study highlights the critical importance of addressing income inequities among residents of public housing with limited financial resources to improve their overall health and social outcomes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.283
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.244
Teacher spread0.229 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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