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Record W4293076169 · doi:10.2196/preprints.36353

Adapting Child Health Knowledge Translation Tools for Indigenous Communities: A Qualitative Exploration of Indigenous Healthcare Provider Perspectives (Preprint)

2022· preprint· en· W4293076169 on OpenAlexaboutno aff
Sarah A Elliott, Jason Kreutz, Kelsey S Wright, Sherri Di Lallo, Shannon D. Scott, Lisa Hartling

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousThematic analysisHealth careNursingKnowledge translationMedicineQualitative researchMedical educationPsychologyPolitical scienceSociologyKnowledge management

Abstract

fetched live from OpenAlex

BACKGROUND Background - Our research groups have developed a number of parent knowledge translation (KT) tools to help families and caregivers 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 healthcare services. It is unclear whether the videos in their current form and language are useful for a wider scope of populations, including Indigenous groups. OBJECTIVE Objectives - The purpose of this study was to explore if and understand how our KT tools could be adapted for use with Indigenous communities. METHODS Methods - Healthcare providers (HCPs) serving Indigenous families in Alberta were asked to review two of our KT tools (one on croup and one on acute otitis media), complete a demographic survey, and participate in a one-on-one semi-structured interview. HCPs were asked to reflect on the usability of our 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 Results - Eighteen HCPs (15 women and 3 men) from various health professions (e.g., doctor, registered nurse, licensed practical nurse, etc.) were interviewed. Seven HCPs self-identified as Indigenous. Four key overarching themes were identified as important when considering how to adapt KT tools for Indigenous communities: accessibility, relatability, KT design, and relationship building. Access to tangible resources and personal and professional connections were considered important. Accessibility impacts the types of KT tools that can be obtained or utilized by various individuals and communities, and the extent to which they can implement recommendations given in those KT tools. Additionally, 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 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 Conclusion - These findings serve to cultivate a greater understanding of the various components to consider when developing and/or culturally adapting KT tools for 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 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)0.026
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 score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.007
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0010.003
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 head0.815
GPT teacher head0.683
Teacher spread0.132 · 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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Citations1
Published2022
Admission routes1
Has abstractyes

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