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Record W4210400460 · doi:10.1186/s12889-021-12412-1

The essential conditions needed to implement the Indigenous Youth Mentorship Program: a focused ethnography

2022· article· en· W4210400460 on OpenAlexafffundabout
Jonathan McGavock, Tamara Beardy, Genevieve Montemurro, Kate Storey

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaQueen's UniversityAlberta HealthUniversity of Alberta
FundersDiabetes CanadaStollery Children’s Hospital FoundationChildren's Hospital FoundationWomen and Children's Health Research InstituteChildren's Health Research InstituteCanadian Institutes of Health ResearchSaskatchewan Health Research FoundationDiabetes Action Research and Education Foundation
KeywordsMentorshipIndigenousAutonomyContext (archaeology)MedicineMedical educationPublic healthNursingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The Indigenous Youth Mentorship Program (IYMP) is a 20-week communal, relationship-based afterschool healthy living program for Indigenous youth in Canada. IYMP embraces the Anishnaabe/Nehiyawak concepts of Mino-Bimaadiziwin/miyo-pimâtisiwin ("living in a good way") via its core components of physical activities/games, healthy snacks, and relationship-building. A strength of IYMP is that it values autonomy, adaptability, and the school community context. However, this presents challenges when evaluating its implementation, given that traditional implementation science methods tend to oversimplify the process. In response, essential conditions for the implementation of school-based healthy living programs across diverse contexts have been developed. The purpose of this research was to understand the applicability of these essential conditions within the context of IYMP. METHODS: 15 participants (n = 10 Young Adult Health Leaders; n = 5 coordinators) with experience implementing IYMP in the provinces of Alberta, Saskatchewan, Manitoba, and Quebec were purposefully sampled. Focused ethnography was the guiding method and one-on-one semi-structured interviews were used as the data generation strategy. The purpose of the interviews was to understand what conditions are needed to implement IYMP. The interview guide was based on previously established essential conditions developed by the research team. Interviews were audio-recorded and transcribed, and content analysis was used to identify patterns in the data. RESULTS: The overarching theme that emerged from the interviews was the applicability of the essential conditions when implementing IYMP. Participants felt the eight core conditions (students as change agents, school/community-specific autonomy, demonstrated administrative leadership, higher-level support, dedicated champion(s) to engage school community, community support, quality and use of evidence, and professional development) and four contextual conditions (time, funding and project support, readiness and understanding, and prior community connectivity) were necessary, but made suggestions to modify two conditions (youth led and learning opportunities) to better reflect their experiences implementing IYMP. In addition, a new core condition, rooted in relationship, emerged as necessary for implementation. CONCLUSIONS: This research adds to the literature by identifying and describing what is needed in practice to implement a communal, relationship-based afterschool healthy living program. The essential conditions may support other researchers and communities interested in implementing and rippling similar programs.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.388
Teacher spread0.313 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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