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Record W2791147741 · doi:10.1080/17441692.2018.1427273

A community-based intervention to build community harmony in an Indigenous Guatemalan Mining Town

2018· article· en· W2791147741 on OpenAlexaff
C. Susana Caxaj, Kolol Qnan Tx’otx’ Parroquia de San

Bibliographic record

VenueGlobal Public Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIndigenousHarmony (color)PoliticsPublic relationsFocus groupPsychological resilienceSociologyCollective efficacyCommunity developmentIntervention (counseling)Political sciencePsychologySocial psychologySocial scienceMedicineNursingLawEcology

Abstract

fetched live from OpenAlex

The presence of large-scale mining operations poses many threats to communities. In a rural community in Guatemala, community leaders were motivated to address divisiveness and local conflict that have been exacerbated since the arrival of a mining company in the region. Prior research by our team identified spiritual and cultural strengths as important sources of strength and resilience in the community. We piloted a community-based intervention centred on spiritual and cultural practices in the region, to address divisiveness and build community harmony. One hundred and seventeen participants from over 18 villages in the municipality participated in the workshops and follow-up focus groups. Community leaders facilitated the intervention and partnered with the academic researcher throughout the research process. Overall, community members and facilitators expressed satisfaction with the workshop. Further, our analysis revealed three important processes important to the development of community harmony in the region: (a) mutual recognition and collectivisation; (b) affirmation of ancestral roots and connections to Mother Earth and (c) inspiring action and momentum towards solutions. These mechanisms, and the socio-political contexts that undermine them, have important implications for how global health programmes are developed and how collective processes for well-being are understood within an inequitable, conflict-laden world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.229
GPT teacher head0.508
Teacher spread0.279 · 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 teacher head, not a consensus.

Study designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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