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Record W4320032608 · doi:10.1007/s41030-023-00218-y

Engaging Ethnically Diverse Populations in Self-Management Interventions for Chronic Respiratory Diseases: A Narrative Review

2023· review· en· W4320032608 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePulmonary Therapy · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEthnically diversePsychological interventionSelf-managementMedicineQuality of life (healthcare)Intervention (counseling)DiseasePopulationGerontologyIntensive care medicineNursingEnvironmental healthPathology

Abstract

fetched live from OpenAlex

The burden of chronic respiratory diseases continues to rise globally. Comprehensive management relies on a combination of treatment approaches including patient self-management, where health professionals are required to educate and support patients to take control of their disease. When self-management interventions are suitably directed and effectively executed, outcomes point to increases in quality of life and a reduction in unscheduled or emergency consultations for people living with chronic respiratory disease. However, despite these positive gains, the literature reveals poor trends of engagement with this management approach and reduced access to appropriately designed programs for people from ethnically diverse populations, including migrants and refugees. The purpose of this review article is to discuss factors influencing engagement in chronic respiratory disease self-management among people from ethnically diverse backgrounds and to propose strategies to improve the participation of this population in these interventions in the future.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.407
GPT teacher head0.592
Teacher spread0.186 · 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