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Record W29476000 · doi:10.2967/jnumed.118.209817

Connecting the Micro and Macro Approaches: Cultural Stakes in Health Communication

2009· article· en· W29476000 on OpenAlexaboutno aff
Isaac Nahón-Serfaty, Rukhsana Ahmed, Sylvie Grosjean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsSociology of health and illnessMedical sociologyPublic healthHealth promotionSociologyPublic relationsField (mathematics)Medical anthropologyPerspective (graphical)Social scienceSocial psychologyPsychologyHealth carePolitical scienceMedicineLawNursing

Abstract

fetched live from OpenAlex

Prior to the establishment of Health Communication as an independent field of study, the socio-cultural dimensions of health and illness (patient/health aide relationship, social representations in health, etc.) were largely covered by sociology and anthropology. (Adam, and Herzlich 1994) Jodelet: “These pioneering disciplines have not failed to integrate a perspective of the individual in their approach, while establishing him in his social and cultural enrolment horizon, or in the framework of his relationship with medical institutions and professionals. ” (2006:1; our translation) However, the recognition of culture in the medical field has already been formulated in 1986 during the Congress of the World Health Organization in Ottawa, Canada. A new health system trend was then defined: the promotion of health that would offer, in addition to clinical and curative services, the recognition and respect of cultural needs. Nonetheless, Jodelet (2006) puts forth the statement that psychology of health was blinded by the game of collective dimensions that intervenes in the individual and public managing of health and illness. Within these dimensions, Jodelet considers culture being almost nonexistent, despite it being a central part of several works in sociology, history, and anthropology, which were concerned with illness and health.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.986

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 studies0.0010.000
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 head0.150
GPT teacher head0.384
Teacher spread0.234 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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