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Record W2901585086 · doi:10.1515/9789048532810-008

7. Muslim migrants in Montreal and perinatal care. Challenging moralities and local norms

2017· book-chapter· en· W2901585086 on OpenAlexaboutno aff
Sylvie Fortin, Josiane Le Gall

Bibliographic record

VenueAmsterdam University Press eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Identity, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyGender studies

Abstract

fetched live from OpenAlex

Stemming from multisited research on Muslim migrants in Montreal and perinatal care, 1 this paper centres on the local and transnational socialities in regards to perinatal knowledge as well as how these socialities and knowledge-sharing practices become actors in the local clinical encounter.We address these two themes within a pluralistic setting, where health services (whether community or tertiary) seek to adjust to local demographic changes (31% of Montrealers are born outside of Canada (Statistics Canada, 2007)). 2 For more than a decade, Muslim countries have been among the leading home countries of Montreal's migrants, making Islam (mostly Sunnite) the second religion in Quebec, after Catholicism.Families of all backgrounds share perinatal knowledge that is passed on from one generation to the next (Cresson and Mebtoul, 2010).Yet, this knowledge changes over time and place (Hjelm et al., 2009; Grewal et al., 2008; Boyacioglu and Trkmen, 2008; Yount, 2007).How it is met within clinical encounters with Muslim migrants gives rise to a number of questions, particularly in regards to gender roles, family dynamics, and decision-making processes related to healthcare issues (Ny et al., 2007, 2008; Pels, 2000; Fortin and Le Gall, 2012; Fortin, 2013b).In this contribution, we discuss socialities in the context of migration, with special attention to the changing role of fathers and how these socialities are involved in the clinical encounter.We see that expert knowledge

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.977

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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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