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Record W4220700754 · doi:10.1177/00178969221087679

African immigrant students’ participation in Canadian health-promoting schools

2022· article· en· W4220700754 on OpenAlexafffundabout
Lawrence Nyika

Bibliographic record

VenueHealth Education Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsSt. Francis Xavier University
FundersNova Scotia Health Research Foundation
KeywordsPhotovoiceFocus groupImmigrationNova scotiaQualitative researchPsychologyPedagogySociologyGender studiesPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Background: Students are key stakeholders in schools, and their participation in the work of health-promoting schools (HPS) is crucial. This study focused on African immigrant students to Canada, who face the unique challenge of navigating unfamiliar school systems. The purpose of the study was to understand how immigrant students imagined, felt and thought about themselves in relation to education and health-related programmes from their perspective as Nova Scotia school stakeholders. Methods: The investigation was informed by critical race theory and social constructivism and involved three research methods: photovoice, individual interviews and focus groups. Study participants were 15 secondary school students of colour, aged between 12 and 21 years, who had migrated to Nova Scotia from Africa and the Caribbean region within the last 10 years. Findings: Three overarching themes were developed from the study relating to: the pedagogy of the HPS, Black consciousness and school health culture. Conclusion: Research participants perceived their participation in HPS as racially and pedagogically challenging, to a variable extent. Study findings highlight the significance to HPS programming of authentically representing Blackness, familiarity or love, and Afro-Caribbean cultural food.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.003
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.082
GPT teacher head0.516
Teacher spread0.434 · 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 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

Citations7
Published2022
Admission routes3
Has abstractyes

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