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Record W3015250385 · doi:10.1371/journal.pone.0231327

The negative self-perceived health of migrants with precarious status in Montreal, Canada: A cross-sectional study

2020· article· en· W3015250385 on OpenAlexafffundabout
Patrick Cloos, Elhadji Malick Ndao, Joséphine Aho, M Benoit, Amandine Fillol, Maria Munoz-Bertrand, Marie-Jo Ouimet, Jill Hanley, Valéry Ridde

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychosocialCross-sectional studySocioeconomic statusMedicineImmigrationDemographySocial determinants of healthLogistic regressionEnvironmental healthGeographyPublic healthPopulationSociologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Knowledge about the health impacts of the absence of health insurance for migrants with precarious status (MPS) in Canada is scarce. MPS refer to immigrants with authorized but temporary legal status (i.e. temporary foreign workers, visitors, international students) and/or unauthorized status (out of legal status, i.e. undocumented). This is the first large empirical study that examines the social determinants of self-perceived health of MPS who are uninsured and residing in Montreal. METHODS AND FINDINGS: Between June 2016 and September 2017, we performed a cross-sectional survey of uninsured migrants in Montreal, Quebec. Migrants without health insurance (18+) were sampled through venue-based recruitment, snowball strategy and media announcements. A questionnaire focusing on sociodemographic, socioeconomic and psychosocial characteristics, social determinants, health needs and access to health care, and health self-perception was administered to 806 individuals: 54.1% were recruited in urban spaces and 45.9% in a health clinic. 53.9% were categorized as having temporary legal status in Canada and 46% were without authorized status. Regions of birth were: Asia (5.2%), Caribbean (13.8%), Europe (7.3%), Latin America (35.8%), Middle East (21%), Sub-Saharan Africa (15.8%) and the United States (1.1%). The median age was 37 years (range:18-87). The proportion of respondents reporting negative (bad/fair) self-perception of health was 44.8%: 36.1% among migrants with authorized legal status and 54.4% among those with unauthorized status (statistically significant difference; p<0.001). Factors associated with negative self-perceived health were assessed using logistic regression. Those who were more likely to perceive their health as negative were those: with no diploma/primary/secondary education (age-adjusted odds ratio [AOR]: 2.49 [95% CI 1.53-4.07, p<0.001] or with a college diploma (AOR: 2.41 [95% CI 1.38-4.20, p = 0.002); whose family income met their needs not at all/a little (AOR: 6.22 [95% CI 1.62-23.85], p = 0.008) or met their needs fairly (AOR: 4.70 [95% CI 1.21-18.27], p = 0.025); with no one whom they could ask for money (AOR: 1.60 [95% CI 1.05-2.46], p = 0.03); with perception of racism (AOR: 1.58 [95% CI 1.01-2.48], p = 0.045); with a feeling of psychological distress (AOR: 2.17 [95% CI 1.36-3.45], p = 0.001); with unmet health care needs (AOR: 3.45 [95% CI 2.05-5.82], p<0.001); or with a health issue in the past 12 months (AOR: 3.44 [95% CI 1.79-6.61], p<0.001). Some variables that are associated with negative self-perceived health varied according to gender: region of birth, lower formal education, having a family income that does not meet needs perfectly /very well, insalubrious housing, not knowing someone who could be asked for money, and having ever received a medical diagnosis. CONCLUSIONS: In our study, almost half of immigrants without health insurance perceived their health as negative, much higher than reports of negative self-perceived health in previous Canadian studies (8.5% among recent immigrants, 19.8% among long-term immigrants, and 10.6% among Canadian-born). Our study also suggests a high rate of unmet health care needs among migrants with precarious status, a situation that is correlated with poor self-perceived health. There is a need to put social policies in place to secure access to resources, health care and social services for all migrants, with or without authorized status.

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 score0.075
Threshold uncertainty score0.352

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 head0.047
GPT teacher head0.302
Teacher spread0.254 · 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

Citations60
Published2020
Admission routes3
Has abstractyes

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