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Record W4308506958 · doi:10.29173/pathways34

Our Lives as Women: An Analytical Framework of What Makes Q'eqchi' Women Sick

2022· article· en· W4308506958 on OpenAlexaffvenue
Natalya Jones

Bibliographic record

VenuePathways · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSocioeconomic statusThematic analysisGender studiesPsychologySociologyQualitative researchSocial scienceDemography

Abstract

fetched live from OpenAlex

The global pattern of women suffering from worse health than men, based on cultural, economic, and biological factors, was found in a small group of Q’eqchi’ women in the Toledo district of Belize. This research follows the health narratives of 20 Q’eqchi’ women to determine what they believe causes their poor health. Through in-depth personal interviews Q’eqchi’ women shared that they suffer from backaches, headaches, pain in their bones, and heavy bleeding. Further, the women also reported ‘thinking too much’ as a factor in their health caused by their reproductive roles. Through thematic analysis and an extensive review of varied literature, this research found that the colonization-rooted Latin gender roles of machismo and marianismo work to sustain Q’eqchi’ women to the domestic sphere. Based on the fact that most Q’eqchi’ women are mothers and wives, these women are stripped of opportunities to obtain education and gain employment, leading to high stress levels and a dependency on their partners for socioeconomic support. Moreover, Q’eqchi’ women’s domestic responsibilities involved arduous physical labour with little rest or help from their male spouses. This labour, combined with the pressures and responsibilities associated with their sex, results in their somatic and psychosomatic expressions of sickness. The research presented in this paper underpins the significance of women’s sex and cultural systems when analyzing global health outcomes. More nuanced considerations of cultural structures, like those mentioned by Q’eqchi’ women, need to be prioritized by policymakers and global health initiatives internationally to better support women’s health.

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)0.009
metaresearch head score (Gemma)0.007
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 score0.037
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0240.063
Scholarly communication0.0140.014
Open science0.0020.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.060
GPT teacher head0.378
Teacher spread0.317 · 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
Published2022
Admission routes2
Has abstractyes

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