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Record W2119625779

Official language minority communities in Canada : is official language minority-majority status associated with mental health problems and mental health service use?

2010· article· en· W2119625779 on OpenAlexfundaboutno aff
Chassidy Puchala

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsMental healthOfficial languageLanguage barrierMinority languagePolitical sciencePsychologyPsychiatryLinguisticsLaw
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Language is a key health determinant that may affect an individual's wellbeing and prevent access to health care services.1 Within Canada, official language use and minority-majority status differs provincially (French-majority/English-minority in Quebec and French-minority/English-majority outside of Quebec).Although the little research that is available indicates that health disparities may exist between French-and English-speaking Canadians, [2][3][4][5][6] the role of both language and minority-majority status has been neglected.Purpose: The first objectives of the current study was to determine whether disparities exist in mental health and mental health service use between minority and majority Canadian Francophone and Anglophone communities both within and outside of Quebec.The second objectives was to examine if official language minority-majority status was associated with the presence of common mental health problems and mental health service utilization.Methodology: The current study used data from the Canadian Community Health Survey: Mental Health and Well-being, Cycle 1.2.7 Two main comparisons were made: Quebec Francophones to Quebec Anglophones, and outside Quebec Francophones to outside Quebec Anglophones.Twelve-month and lifetime prevalences of mental disorders and mental health service use were examined through bivariate analyses.Logistic regression analyses determined whether official language minority-majority status significantly predicts mental health problems and mental health service use using the Determinants of Health Model 8-10 and Andersen's behavioural model.[11][12][13] Results: Very few significant differences were found between official language groups both outside and within Quebec, though some notable differences were found between Quebec and outside Quebec: Anglophones and Francophones outside Quebec had a higher prevalence of iii poor mental health and low life satisfaction compared their respective language counterparts in Quebec.Respondents from outside Quebec had a higher prevalence of consulting with a psychiatrist than respondents from Quebec.There was no significant association between membership in an Official Language Minority Community and mental health problems, and mental health service use.Implications: Although our results indicate that very few differences exist between official language minority and majority groups, these findings remain important and can help aid key stakeholders redirect resources and develop policies and programs towards areas and geographic locations wherein health disparities exist.Dr. Bonnie Janzen.Your expertise has been most valuable and you both have gone above and beyond to aid me in each step of this process.To Dr. Scott Patten, I am grateful for your participation as my external committee member.Without Statistics Canada, this project would not have been possible.Specifically, I would like to thank the Saskatchewan Research Data Centre and Jesse McCorsky who were extremely helpful and knowledgeable regarding all aspects of my analyses.

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)0.004
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 score0.019
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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
Published2010
Admission routes2
Has abstractyes

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