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Record W2137558901 · doi:10.1177/1049732304269671

Developing Resilience: How Women Maintain Their Health in Northern Geographically Isolated Settings

2004· article· en· W2137558901 on OpenAlexaffabout
Beverly Leipert, Linda Reutter

Bibliographic record

VenueQualitative Health Research · 2004
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of AlbertaWestern University
Fundersnot available
KeywordsVulnerability (computing)Psychological resiliencePsychosocialGrounded theoryQualitative researchHealth careSocioeconomicsEnvironmental healthGeographySociologyEconomic growthPsychologyMedicineSocial psychologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore how women maintain their health in northern geographically isolated settings, using a feminist grounded theory method. Twenty-five women of diverse backgrounds in northern British Columbia, Canada, engaged in qualitative interviews over a 2-year period to express perspectives about how the north affects their health and how they maintain their health in northern settings. Findings reveal that the women experienced vulnerability to physical health and safety risks, psychosocial health risks, and risks of inadequate health care. The women responded to these vulnerabilities by developing resilience through the strategies of becoming hardy, making the best of the north, and supplementing the north. These strategies, which reflect both individual and collective actions, were determined by the needs and interests of the women and their social and personal resources. The findings have implications for women's health research and health practices and policies in geographically isolated settings.

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)0.003
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 score0.120
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.001
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 head0.193
GPT teacher head0.567
Teacher spread0.374 · 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

Citations97
Published2004
Admission routes2
Has abstractyes

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