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Record W2137277272 · doi:10.1002/jcop.20251

Mapping nondominant voices into understanding stress‐coping mechanisms

2008· article· en· W2137277272 on OpenAlexaffabout
Yoshitaka Iwasaki, Judith Bartlett, Kelly J. MacKay, Jennifer Mactavish, Janice Ristock

Bibliographic record

VenueJournal of Community Psychology · 2008
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIntersectionalityCoping (psychology)StressorPsychologySocial psychologyNarrativeMinority stressGender studiesDevelopmental psychologySociologyClinical psychology

Abstract

fetched live from OpenAlex

Abstract This study reports key findings from a research project, which examined the stress and coping mechanisms of several nondominant groups of individuals. The groups were based in Winnipeg, Manitoba, Canada and included (a) Aboriginal individuals with diabetes, (b) individuals with disabilities, and (c) gays and lesbians. Our analyses of personal narratives and life stories have led to develop an interpretive map of findings that depicts mechanisms of how stress and coping operate. Specifically, the interpretive map consists of personal and structuralstressors, meanings of stress, and personal and structuralresources, as well as of two constructs termedintersectionalityandsocial exclusion. Not only are nondominant voices and lived experiences recognized and incorporated into an emergent interpretive map, but this map also articulates the complex ways in which multiple identities intersect (i.e., intersectionality) and the realities of being excluded socioeconomically, culturally, and politically among nondominant groups (i.e., social exclusion). © 2008 Wiley Periodicals, Inc.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.007
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.525
GPT teacher head0.506
Teacher spread0.019 · 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

Citations14
Published2008
Admission routes2
Has abstractyes

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