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Record W2759586941 · doi:10.18357/ijih122201717783

An Exploration of the Effects of Mentor-Apprentice Programs on Mentors' and Apprentices' Wellbeing

2017· article· en· W2759586941 on OpenAlexvenueaboutno aff
Barbara Jenni, Adar Anisman, Onowa McIvor, Peter Jacobs

Bibliographic record

VenueInternational Journal of Indigenous Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsApprenticeshipIndigenousIndigenous languageFocus groupExploratory researchPsychologyMedical educationPedagogySociologyMedicineSocial scienceGeographyAnthropology

Abstract

fetched live from OpenAlex

Increasingly, adult Indigenous language learners are being identified as the “missing generation” of learners who hold great potential to contribute to the revival of Indigenous languages by acting as the middle ground between Elders, children, and youth within their communities. Our research project NEȾOLṈEW̱ “one mind, one people” studied adult Indigenous language learning through the popular Mentor-Apprentice Program method. Over a 2-year period, our team conducted interviews and focus groups with participants involved in a Mentor-Apprentice type program in British Columbia, Canada. While our primary interest was to document the successes and challenges of the Mentor-Apprentice Program method for adult Indigenous language learning, we also included interview questions that gave participants an opportunity to share how participating in such a program affected them. During data analysis, we noticed repeating comments from participants about how their involvement with a Mentor-Apprentice Program impacted their own and their community’s wellbeing; 6 exploratory themes were identified. Although studies have reported protective effects of Indigenous language use on health, health-related outcomes of language revitalization efforts remain underexplored. In addition to discussing the exploratory themes that arose from the study, our paper also proposes that these themes can inform future research in investigating the links between language revitalization and wellbeing.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.362
Teacher spread0.339 · 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 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

Citations40
Published2017
Admission routes2
Has abstractyes

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