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Record W4382049096 · doi:10.21203/rs.3.rs-3016905/v1

Revitalizing Strong Cultural Connections and Resilience: Co-Designing a Pilot Elder-Led Mentorship Program for Indigenous Mothers in a Remote Northern Community in Alberta, Canada

2023· preprint· en· W4382049096 on OpenAlexafffundabout
Kayla Fitzpatrick, Stephanie Montesanti, Barbara Verstraeten, Beverly Tourangeau, Lorraine Albert, Richard T. Oster

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAlberta Health ServicesUniversity of Alberta
FundersCanadian Institutes of Health ResearchUniversity of AlbertaAlberta Health Services
KeywordsIndigenousCultural safetyThematic analysisGeneral partnershipParticipatory action researchTraditional knowledgeNursingPublic relationsMedical educationMedicineSociologyPsychologyPolitical scienceQualitative researchSocial science

Abstract

fetched live from OpenAlex

Abstract Background: Connection to traditional knowledge and culture is important for promoting Indigenous parental well-being and fostering healthy environments for child development. Historical and modern injustices have resulted in a loss of culture, language, identity, spiritual and traditional practices in birth and parenting for many Indigenous peoples across the globe. Community Elders in a remote northern community in Alberta, Canada, and researcher allies collaborated to design a pilot Elders Mentoring Program to support Indigenous mothers(-to-be) and bring back cultural traditions, teachings and Indigenous knowledge on motherhood. Methods: Community-based participatory research principles guided all aspects of the research partnership. Elders and researchers organized 12 workshops with Indigenous mothers(-to-be) centred on traditional activities including beading, sewing, and medicine picking in conjunction with traditional knowledge transfer and cultural teachings from Elders on aspects of well-being. An explanatory mixed methods study design was used for this project. Quantitative data was collected from surveys completed by the mothers (n=9) at the start of the program about perinatal and postpartum health experience, determinants of care needs, and aspects of resilience of Indigenous mothers. To capture the Elders and mothers’ experiences from their participation in the project, qualitative data was collected using a sharing circle with Elders and individual debrief conversations with mothers. Survey responses were analysed descriptively and sharing circles and debrief conversations were analysed using thematic content analysis. Results: Survey results showed limited availability of services, transportation and access to childcare as factors determining access and utilisation of perinatal and postpartum services and programs. Four themes emerged from qualitative data analysis: (1) the meaningful role of Elder mentorship for Indigenous mothers(-to-be); (2) weekly workshops provided a safe space to share and develop peer-to-peer relationships; (3) passing on of traditional stories and skills during participation in cultural activities fostered positive coping and self-esteem among the Elders and mothers; (4) Elder-led workshops encouraged culture and language revitalization and passing on of tradition to younger generations. The project was positively perceived by Elders and mothers who participated. Conclusions: The findings demonstrated that Elder-led cultural workshops promoted cultural connectedness and enhanced resilience for mothers(-to-be) in a remote northern Indigenous community.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.502
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.002
Scholarly communication0.0010.001
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.443
Teacher spread0.338 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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