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Record W4243314058 · doi:10.22215/etd/2017-12647

Political Ecology of Health of South Sudanese Immigrants and Refugees in Ottawa

2017· dissertation· en· W4243314058 on OpenAlexfundaboutno aff
Kathryn P. MacPherson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersNipissing University
KeywordsSolidarityRivalryPoliticsFeelingFocus groupRefugeeQualitative researchImmigrationEthnic groupGender studiesPolitical scienceSociologyGeographySocial psychologyPsychologySocial scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

Emerging research suggests that despite access to health care, South Sudanese in Canada experience dismal health.Applying political ecology of health (PEH) framework, this qualitative study builds on literature, to examine factors underlying this health decline.Focus groups and in-depth interviews with South Sudanese in Ottawa (n=31) reveal multiple factors acting alone or in interaction with processes at multiple temporal and spatial scales.Specifically, findings show that trauma suffered before arrival and from ongoing conflict in South Sudan affects everyday health.The findings also show that a deep political and moral sentiment of solidarity with the motherland mediates household decisions about health.In addition, fierce intra-ethnic rivalry stirred by war in South Sudan greatly affects South Sudanese, eroding social and psychological resources necessary for health.Furthermore, the findings indicate that weak integration of South Sudanese men in Canada breeds feelings of loss of social status, triggering family instability and gendered health impacts.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0030.001
Open science0.0010.004
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 head0.022
GPT teacher head0.386
Teacher spread0.363 · 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 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

Citations0
Published2017
Admission routes2
Has abstractyes

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