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Record W3071639952 · doi:10.1093/pch/pxaa068.094

95 Child Health Promotion Through Community Educational Sessions in an Urban Inuit Community: A Needs Assessment

2020· article· en· W3071639952 on OpenAlexaboutno aff
Brian Hummel, Daniel Bierstone, Radha Jetty, Dennis Newhook, Janice Messam, Trish Beadle, Stephanie Sutherland

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupIndigenousMedicineHealth promotionNeeds assessmentFamily medicinePublic healthNursingSociology

Abstract

fetched live from OpenAlex

Abstract Introduction/Background Canadian Inuit children experience significant health disparities compared to their non-Inuit counterparts. Despite almost one-fifth of Canadian Inuit living in urban centres, few studies have explored their health needs. Current literature surveying Indigenous leaders identifies the need for improved access to child health and parenting knowledge. Community-based initiatives have been shown to improve Indigenous maternal and child health outcomes. Our study aimed to describe urban Inuit parents’ perspectives on accessing child health knowledge to guide development of Inuit-specific health knowledge-sharing initiatives. Objectives Design/Methods In conjunction with community partners, we conducted a qualitative needs assessment through focus groups at an urban-situated organization that provides cultural, educational, and social services to Inuit children and families. Participants were parents and caregivers of Inuit children. All focus groups were recorded, transcribed, and imported into NVivo software. Inductive coding was used to identify themes related to participants’ sources of health knowledge, barriers and facilitators to accessing health knowledge, and health topics that participants hoped to learn more about. Results Twenty-four individuals participated in four focus groups, of which twenty-one (88%) identified as Inuit. While participants represented a range of ages (19-40 years), most participants (42%) were 31-40 years old. The majority of participants (88%) identified as female. Participants had lived a median of 15 years in an urban setting (interquartile range 10-23). Seventeen participants (71%) cared for children aged 5 or younger. The main sources of health knowledge reported were Indigenous-focused services, online resources, telehealth and social networks (e.g. family and peers). The most notable barrier to accessing child health information was cultural differences (i.e. lifestyle and parenting practices). Discrimination and challenges with systems navigation also emerged as themes. Key health topics of interest included common childhood complaints (e.g. infections and immunizations), infant care, nutrition, parenting and development, mental health, and sexual education for adolescents. Preferred modes of child health information delivery were in-person sessions, pamphlets, and online videos with preferences for both health care providers and Inuit Elders as facilitators. Key access factors included Inuit language/translation, convenience of location, transportation, scheduling, and presence of food and childcare. Conclusion Our results reveal important factors affecting access to child health knowledge among Inuit families in a large urban setting, as well as key child health topics of interest to this population. Informed by these findings and with help from our community partners, we are co-developing child health knowledge-sharing initiatives specific to the needs of the Inuit community in our region.

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 score0.732
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.088
GPT teacher head0.422
Teacher spread0.334 · 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
Published2020
Admission routes1
Has abstractyes

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