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Record W2211877419 · doi:10.1177/117718011501100407

Shared Decision Making with Aboriginal Women Facing Health Decisions: A qualitative study identifying needs, supports, and barriers

2015· article· en· W2211877419 on OpenAlexaffabout
Janet Jull, Audrey R. Giles, Yvonne Boyer, Minwaashin Lodge, Dawn Stacey

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsAboriginal Affairs Northern Dev CanadaBruyèreUniversity of Ottawa
Fundersnot available
KeywordsThematic analysisQualitative researchPsychological interventionCitizen journalismEquity (law)Health carePsychologyParticipatory action researchMedical educationPublic relationsSociologyMedicineNursingPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Shared decision making (SDM) may narrow health equity gaps by engaging clients with their health care providers in decision making; little is known about SDM interventions with Aboriginal people. This study describes the health decision-making experiences of Aboriginal women by identifying decision needs, supports, and barriers. An interpretive descriptive qualitative study was conducted from January to June 2013 with an advisory group using a mutually developed ethical framework, participatory research principles, and postcolonial theory. Aboriginal women at Minwaashin Lodge were interviewed in semi-structured interviews and transcripts were coded using thematic analysis. Participants were 13 women between 20 and 70 years of age, and of Inuit, Métis, or First Nations descent. SDM needs and supports are represented by themes focused on relational features of SDM, and presented in a Medicine Wheel framework. Findings indicate that to be relevant for Aboriginal women, SDM tools and approaches may need to be adapted, and participatory approaches must be used.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.187
GPT teacher head0.511
Teacher spread0.324 · 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

Citations39
Published2015
Admission routes2
Has abstractyes

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