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Record W4384299691 · doi:10.32799/ijih.v18i1.39255

It’s more than just physical: Experiences of pain and pain management among Māori with cancer and their whānau.

2023· article· en· W4384299691 on OpenAlexvenueno aff
Virginia Signal, Rhiannon Jones, Cheryl Davies, Jeannine Stairmand, Moira Smith, Jonathan M. Adler, Jason Gurney

Bibliographic record

VenueInternational Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersHealth Research Council of New Zealand
KeywordsAotearoaCancer painContext (archaeology)Pain managementQualitative researchScope (computer science)MedicineFocus groupHealth careNursingPsychologyCancerPhysical therapySociologyPolitical scienceGender studiesSocial scienceHistoryInternal medicine

Abstract

fetched live from OpenAlex

This study investigated the experiences of pain and pain management among Māori with cancer in Aotearoa New Zealand. Using a qualitative study design underpinned by kaupapa Māori research principles, focus group hui and interviews were held with Māori with cancer and their whānau (n=24). We identified themes relating to holistic experiences of pain and pain management, the importance of appropriate support and good communication, and intertwined cancer and pain journeys that impact holistically on Māori with cancer and their whānau. We argue that Aotearoa’s health care system must expand the scope of what pain and pain management means in the context of cancer and act accordingly by adequately supporting Te Ao Māori-centred approaches. The health care system must also heed the call for culturally responsive pain management for Māori, which is especially important when caring for whānau with a disease where pain - both physical and non-physical - is a common and significant symptom.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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