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Record W2613978575

Life Experiences and Patterns of Distress in Chinese-Canadian Women with a History of Suicidal Behaviour

2015· dissertation· en· W2613978575 on OpenAlexaboutno aff
Juveria Zaheer

Bibliographic record

VenueTSpace (University of Toronto) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
Fundersnot available
KeywordsDistressPsychologyPsychological distressClinical psychologyPsychiatryMedicineDevelopmental psychologyMental health
DOInot available

Abstract

fetched live from OpenAlex

Recent studies have highlighted higher rates of suicidal ideation and behaviour and associated themes of gender role stress in Chinese women residing in North America (Chung, 2004). Through qualitative interviewing and analysis, this study explores the experiences, stressors and beliefs of Chinese-born women living in Canada with a history of suicidal behaviour. They describe restricted patterns of emotional communication, feelings of lack of agency, experiences of victimization and oppression and stress related to traditional gender expectations and those related to social change. Expectations of immigration often go unmet and stress arises from financial, educational and family pressures. As the women struggle to endure this distress, they experience a negative view of self, worsening depressive symptoms and hopelessness. They come to a "breaking point" leading to suicidal behaviour that can be understood as an escape from pain, a strategy to communicate distress and a consequence of pervasive hopelessness.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.293

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 studies0.0050.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.298
Teacher spread0.277 · 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
Published2015
Admission routes1
Has abstractyes

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