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Record W4221012533 · doi:10.2196/32325

The Impact of COVID-19 on the Delivery of Educational Programs in Native American Communities: Qualitative Study

2022· article· en· W4221012533 on OpenAlexvenueno aff
Lea Sacca, Christine Markham, Belinda Hernandez, Ross Shegog, Melissa F. Peskin, Stephanie Craig Rushing, Hannah Warren, Monique Tsosie

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisExploratory researchPandemicQualitative researchCurriculumMedical educationHealth equityPsychologySocioeconomic statusMental healthCoronavirus disease 2019 (COVID-19)Public relationsMedicineSociologyNursingPolitical sciencePedagogyPublic healthEnvironmental healthPopulationSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the availability of culturally responsive sexual health educational programs for American Indian and Alaska Native (AI/AN) youth, barriers to their uptake and utilization persist in tribal communities. These challenges were exacerbated by the COVID-19 pandemic, which required flexible program delivery using both in-person and virtual classrooms. OBJECTIVE: This exploratory study provides a preliminary understanding of the extent to which pre-existing challenges impact the delivery of culturally responsive sexual health education programs in Native communities and to what extent they were exacerbated by the COVID-19 pandemic. It also highlights the challenges faced by adolescent health advocates when adapting culturally responsive health curricula to online platforms. Finally, this study discloses major socioeconomic, health, and mental challenges experienced by AI/AN youth during the pandemic. METHODS: An exploratory, descriptive, qualitative design approach was adopted to carry out 5 individual and 1 collective in-depth key informant interviews. A total of 8 Native and non-Native sexual health educators served as key informants and shared their personal experiences with the delivery of sexual health education programs for youth during the COVID-19 pandemic. The interviews were conducted virtually from October to November 2020 using Zoom to reach participants dispersed across different regions of the United States. We followed the consolidated criteria for reporting qualitative research (COREQ) as a reference for the study methodology. We also used the Braun and Clarke framework (2006) to conduct a thematic analysis. RESULTS: Experts' opinions were structured according to 5 main themes: (1) competing community priorities during COVID-19; (2) moving to web-based programming: skills, training, support; (3) recruiting youth; and (4) challenges for implementation in a household environment; and (5) recommendations to overcome implementation challenges. These themes are complementary, connected, and should be considered holistically for the development, dissemination, and implementation of online sexual health programs for AI/AN youth, specifically during the COVID-19 pandemic. The results raised the following points for discussion: (1) Building partnerships with schools and community organizations facilitates program adaptation and implementation, (2) there is a need to adopt a holistic approach when addressing youth sexual health in AI/AN communities, (3) a systematic and culturally responsive adaptation approach ensures effective virtual program delivery, and (4) community and youth engagement is essential for the success of virtual sexual health programs. CONCLUSIONS: Findings can provide recommendations on actions to be taken by sexual health educators and guidelines to follow to ensure cultural sensitivity, effective adaptation, and successful implementation when setting out to advocate for online sexual health programs for AI/AN youth.

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)0.013
metaresearch head score (Gemma)0.016
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0140.008
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
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 head0.572
GPT teacher head0.670
Teacher spread0.097 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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