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Record W4249611063 · doi:10.24124/2015/bpgub1018

Intergenerational knowledge transmission from aboriginal female elders to youth regarding preventative and self-care knowledge of urinary tract infections in Prince George, BC.

2015· dissertation· en· W4249611063 on OpenAlexfundaboutno aff
Jennifer L. Nguyen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsGeorge (robot)NarrativeTransmission (telecommunications)Interpretative phenomenological analysisGerontologyGender studiesMedicinePsychologySociologyQualitative researchSocial scienceHistoryArtEngineering

Abstract

fetched live from OpenAlex

This study examines the factors influencing intergenerational knowledge transmission of Aboriginal women in Prince George, BC regarding preventative and self-care practices for urinary tract infections. The research questions were: What is the level of intergenerational knowledge exchange between female Aboriginal elders and youth, and what are the factors influencing this transmission? Interviews were conducted with seven Aboriginal youth and three elders living in Prince George and analyzed using the combined methods of narrative inquiry and interpretive phenomenological analysis. The results indicate that there are both historical and contemporary factors influencing the level of intergenerational knowledge transmission. Some participants discussed the complexities of finding their footing in a new territory, and the cultural tensions they felt as Aboriginal outsiders within a new Aboriginal community. Participants offered recommendations for improving the level of Aboriginal knowledge transmission and implications for future research were discussed. --Leaf ii.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.828
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.019
GPT teacher head0.371
Teacher spread0.352 · 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
Published2015
Admission routes2
Has abstractyes

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