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Record W4282581837 · doi:10.1097/ans.0000000000000428

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2022· article· en· W4282581837 on OpenAlexaffabout
Tara C. Horrill, Donna Martin, Josée G. Lavoie, Annette Schultz

Bibliographic record

VenueAdvances in Nursing Science · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of ManitobaResearch ManitobaUniversity of British ColumbiaManitoba Health
Fundersnot available
KeywordsMEDLINEMedicineInternet privacyMedical emergencyComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Inequitable access to oncology care is a significant issue among Indigenous Peoples in Canada; however, the perspectives of oncology nurses have not been explored. Guided by an interpretive descriptive methodology, we explored nurses' perspectives on access to oncology care among Indigenous Peoples in Canada. Nurses described the health care system as "broken" and barriers to accessing oncology care as layered and compounding. Lack of culturally safe care was articulated as a significant issue impacting equitable access, while biomedical discourses were pervasive and competed with nurses' attempts at providing culturally safe and trauma- and violence-informed care by discounting the relational work of nurses.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.777
Threshold uncertainty score0.746

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2230.027

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 head0.024
GPT teacher head0.452
Teacher spread0.428 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2022
Admission routes2
Has abstractyes

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