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Record W4408137018 · doi:10.22605/rrh9522

Critical analysis of interprofessional student-led community health promotion workshops

2025· article· en· W4408137018 on OpenAlexaffabout
Catherine O’Connor, A Labelle, Tyler Pretty, Kayla Katerynuk, Gayle Adams-Carpino

Bibliographic record

VenueRural and Remote Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsNOSM UniversityUniversity of Sudbury
Fundersnot available
KeywordsHealth promotionMedicineInterprofessional educationPromotion (chess)Medical educationEnvironmental healthPublic healthHealth careNursingPolitical science

Abstract

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INTRODUCTION: Health promotion interventions can empower communities and individuals by focusing on social and environmental interventions, rather than on individual behaviour changes. The settings-based approach, rooted in WHO's Health for All initiative, emphasizes community involvement, collaboration, and equity. Community-based health promotion, especially in rural and remote areas where there is a higher proportion of underserved populations, can leverage community assets and promote health equity. Student-led health promotion initiatives are gaining traction, benefiting both students and communities. Reach Accès Zhibbi (RAZ), a student-led organization in Sudbury, Ontario in Canada delivers evidence-based health promotion workshops to vulnerable populations, promoting health literacy and equity. This study examines the impact of RAZ's workshops, addressing a gap in research on student-led, non-clinical health promotion efforts. METHODS: This cross-sectional mixed-methods study examined RAZ workshops at five partnering community agencies. Data was collected with two surveys: a web-based survey for staff and a paper-based survey for workshop participants. The first gathered perspectives on long-term impacts of the workshops, while participant surveys were given before and after the workshops to assess baseline knowledge, learning, and behavioural intent. The surveys were developed using the Health Behaviour Scale-16 and were designed at a grade 5 reading level for accessibility. Data analysis involved frequency analysis and Wilcoxon signed-rank test to assess perceived learning gains. Thematic analysis was conducted on qualitative data. RESULTS: Seven employees from three of the five partnering agencies rated the effectiveness of RAZ workshops, with a mean score of 9 out of 10. They highlighted benefits such as increased knowledge, skills, and mental wellness. Thematic analysis identified three key themes: long-term impact, practical application, and mutual collaboration. Among 33 workshop participants, significant improvements were observed in health literacy, decision-making, and physical and mental health knowledge post-workshop. A Wilcoxon signed-rank test on adjusted change scores for pre- and post-workshop data revealed statistically significant gains in perceived learning across all aspects. Most attendees found the workshop helpful, with 57.6% planning behaviour changes. CONCLUSION: This study showed that interprofessional student-led health promotion workshops effectively enhance health literacy and empower underserved communities. Significant improvements in participants' knowledge and confidence suggest these workshops help address health disparities. The findings highlight their potential scalability and adaptability across communities, promoting sustainable health promotion efforts, an important consideration for rural and remote communities.

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)0.059
metaresearch head score (Gemma)0.210
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 score0.059
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.210
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.007
Science and technology studies0.0050.003
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.503
Teacher spread0.444 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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