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Record W2898539759 · doi:10.32799/ijih.v13i1.30300

Adult Māori Patients’ Healthcare Experiences of the Emergency Department in a District Health Facility in New Zealand

2018· article· en· W2898539759 on OpenAlexvenueno aff
Sneha Abraham, Marama Tauranga, Deborah Moore

Bibliographic record

VenueInternational Journal of Indigenous Health · 2018
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaIndigenousEmergency departmentHealth careNursingHealth professionalsHealthcare serviceQualitative researchHealth departmentMedicineHealth equityFamily medicinePsychologySociologyPolitical sciencePublic healthGender studies

Abstract

fetched live from OpenAlex

Globally, there are significant inequalities and disparities in health service delivery to Indigenous populations, including Māori in Aotearoa/New Zealand. This study explored the experiences of adult Māori patients in the emergency department (ED) of a district health facility in New Zealand. Qualitative research exploring the ED experiences of Māori patients is limited. Two semistructured interviews with 4 Māori participants were conducted, audio-recorded, transcribed, and thematically analysed with the help of the Māori health department within the hospital. The participants identified 3 main areas of improvements relating to (a) the ED environment, (b) the interactions with healthcare professionals (HCPs), and (c) the unique factors faced by the kaumātua (Māori elders). The main conclusions were that aspects of the ED environment, including the room layout and lack of privacy, could negatively influence Maori ED experiences. In addition, HCPs not adequately integrating the Māori view of health in their clinical practice also had a negative influence. The kaumātua faced unique challenges, including the language barrier and lack of sufficient information from HCPs during their patient journey. Educating HCPs and making the ED environment more sensitive to Māori could improve their experience.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.336
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2018
Admission routes1
Has abstractyes

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