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Record W4294754300 · doi:10.2196/36353

Adapting Child Health Knowledge Translation Tools for Use by Indigenous Communities: Qualitative Study Exploring Health Care Providers’ Perspectives

2022· article· en· W4294754300 on OpenAlexaffvenueabout
Sarah A Elliott, Jason Kreutz, Kelsey S Wright, Sherri Di Lallo, Shannon D. Scott, Lisa Hartling

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsStollery Children's HospitalCochraneUniversity of Alberta
Fundersnot available
KeywordsIndigenousThematic analysisNursingHealth careKnowledge translationCultural safetyMedicineQualitative researchAotearoaAcute careMedical educationPsychologySociologyKnowledge managementPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Our research groups have developed a number of parental knowledge translation (KT) tools to help families understand common childhood illnesses and make informed decisions regarding when to seek urgent care. We have developed a series of videos to help parents understand how to manage common acute childhood illnesses at home and when to contact emergency health care services. It is unclear whether the videos in their current form and language are useful for a wider range of populations, including Indigenous groups. OBJECTIVE: The purpose of this study was to explore whether and understand how our KT tools could be adapted for use with Indigenous communities. METHODS: Health care providers (HCPs) serving Indigenous families in Alberta, Canada, were asked to review 2 of our KT tools (one on croup and one on acute otitis media), complete a demographic survey, and participate in a one-on-one semistructured interview. HCPs were asked to reflect on the usability of the KT tools within their practice and what cultural adaptation considerations they felt would be needed to develop KT tools that meet the needs of Indigenous clients. Audio recordings from the interviews were transcribed verbatim and analyzed for relevant themes using thematic analysis. RESULTS: A total of 18 HCPs (n=15, 83% women and n=3, 17% men) from various health professions (eg, physician, registered nurse, and licensed practical nurse) were interviewed. Of these 18 HCPs, 7 (39%) self-identified as Indigenous. Four overarching themes were identified as important when considering how to adapt KT tools for use by Indigenous communities: accessibility, relatability, KT design, and relationship building. Access to tangible resources and personal and professional connections were considered important. Accessibility affects the types of KT tools that can be obtained or used by various individuals and communities and the extent to which they can implement recommendations given in those KT tools. In addition, the extent to which users relate to the depictions and content within KT tools must be considered. The environments, portrayals of characters, and cultural norms and values presented within KT tools should be relevant to users to increase the relatability and uptake of recommendations. Most importantly, fostering genuine and sustainable relationships with users and communities is a vital consideration for KT tool developers. CONCLUSIONS: These findings serve to cultivate a greater understanding of the various components that HCPs consider important when developing or culturally adapting KT tools for use by Indigenous families. This information will help support the effective adaptation and distribution of KT tools for use by a broad audience. Careful consideration of the themes identified in this study highlights the importance of working together with the knowledge users (health care consumers) when developing KT tools.

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)0.023
metaresearch head score (Gemma)0.027
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 score0.109
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0140.009
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.364
GPT teacher head0.526
Teacher spread0.162 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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