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Record W4280606394 · doi:10.3389/fpain.2022.857624

Clinical Strategies to Develop Connections, Promote Health and Address Pain From the Perspectives of Indigenous Youth, Elders, and Clinicians

2022· article· en· W4280606394 on OpenAlexafffundabout
Rachel VanEvery, Margot Latimer, Angela Naveau

Bibliographic record

VenueFrontiers in Pain Research · 2022
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsDalhousie UniversityAboriginal Affairs Northern Dev CanadaMcMaster University
FundersCanadian Institutes of Health Research
KeywordsIndigenousParticipatory action researchThematic analysisGeneral partnershipHealth careNursingPsychologyConversationMedical educationMedicinePublic relationsSociologyQualitative researchPolitical science

Abstract

fetched live from OpenAlex

In this article we discuss findings from a community based, participatory action research study. The aim was to understand how Indigenous youth describe, experience, manage pain and hurt and how they seek care. A critical analysis guided by Two-Eyed Seeing and Medicine Wheel frameworks highlighted important clinical strategies for Indigenous youth to balance their health and reduce pain. This study is a partnership project with an Aboriginal Health Centre in Southern Ontario and the Canadian Institute of Health Research funded Aboriginal Children's Hurt and Healing Initiative (ACHH). The study gathered perspectives of Indigenous youth, Elders, and health clinicians using conversation sessions guided by a First Nations doctoral student and nurse researcher. Using the medicine wheel framework three main thematic areas emerged across the three groups and include (1) Predictors of Imbalance; (2) Indicators of Imbalance; and (3) Strategies to re-establish balance health in relation to pain. The main strategy includes considerations for clinicians using the acronym LISTEN ( Language, Individual, Share, Teachable moments, Engage, and Navigate ) approach that outlines strategies for clinicians that will be a safe guide to manage pain and hurt.

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 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)0.041
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.420
Teacher spread0.328 · 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 teacher head, not a consensus.

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

Citations10
Published2022
Admission routes3
Has abstractyes

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