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The Power of Collaborative Inquiry and Metaphor in Meeting the Health Literacy Needs of Rural Immigrant Women

2017· book-chapter· en· W2481226959 on OpenAlexaffabout
Al Lauzon, Rachel Farabakhsh

Bibliographic record

VenueIGI Global eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsParticipatory action researchImmigrationLiteracyPresentational and representational actingPedagogyMedical educationCitizen journalismPsychologySociologyPublic relationsMedicinePolitical scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Rural communities often face the need to reach out to immigrant groups to help sustain their populations. However, rural communities often lack the necessary support and resources required to meet the needs of immigrant communities. This chapter reports on the role of a participatory education project in meeting the needs of immigrant Old Colony Mennonite women. Building on an existing ESL program in a rural community in Southwestern Ontario, a participatory health literacy pilot project was developed employing an action research format. With the participants, the authors explored the participant identified topic of dealing with the stress of parenting, using metaphors (presentational knowing) and collaborative inquiry. Post-project, in-depth, semi-structured interviews were completed with participants and program staff. Interview data was analyzed using a constant comparison method and five themes are identified and discussed: (1) reconsidering the nature of their children; (2) the power of language to transform; (3) modeling with language; (4) changing parental behaviours; and (5) normalizing what happens at home. The authors then discuss the efficacy of utilizing presentational knowing and collaborative inquiry as a pedagogical strategy for meeting the learning needs of rural immigrants.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.892
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.321
Teacher spread0.296 · 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.

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
Published2017
Admission routes2
Has abstractyes

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