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Cultural Competence for Health Policy Development

2018· article· en· W2803053226 on OpenAlexaboutno aff
Catherine Clutton

Bibliographic record

VenueThe International Journal of Community Diversity · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCultural competenceCompetence (human resources)Political scienceSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

This article addresses conditions for inclusive policy making in multicultural societies. It focuses on and compares development of health policies in Australia and Canada, asking whether paying greater attention to cultural competence could enhance deliberative health policy development by improving inclusion of people from culturally and linguistically diverse (CALD) backgrounds. Answering this question brings together insights suggested by critical multiculturalism, deliberative democracy, and public administration. Reviewing policy frameworks and interviewing health policy officers in both countries underpinned a critical examination of national and sub-national governments to understand barriers to and enablers of inclusive citizen engagement. Defining culture as relational and changeable, it became apparent that many Australian and Canadian health agencies perceive multicultural policy as a means of managing and controlling diversity rather than a mechanism to improve democratic participation. A critical multicultural perspective promotes consideration of context, challenging individual and organizational histories and assumptions, organisational processes and procedures, to understand how current modes of operating and thinking impact on CALD citizens. The article suggests shifting focus from "cultural competence" towards contextual sensitivity, the promise of which lies in encouraging awareness of citizens as individuals, for whom culture is just one of many influences shaping their position in society.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.145
GPT teacher head0.445
Teacher spread0.300 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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