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Record W7029643646

Les défis de la cybersanté : la perception du risque en matière de développement et de gestion des dossiers électroniques des patients dans le système de santé au Québec

2023· other· fr· W7029643646 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2023
Typeother
Languagefr
FieldArts and Humanities
TopicInterdisciplinary Studies: Technology, Society, and Humanities
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsContext (archaeology)ForgeSocial impact
DOInot available

Abstract

fetched live from OpenAlex

En introduisant les technologies de l’information et de la communication au secteur de la santé, le Québec s’est heurté à des enjeux de gouvernance à travers sa tentative de conjuguer les risques technologiques et les ambitions de la cybersanté. Le processus d’informatisation des dossiers médicaux, négocié par une variété d’acteurs collectifs depuis près de 30 ans, s’est soldé par une disjonction des composantes de l’écosystème numérique que l’on tente toujours aujourd’hui de pallier. Cherchant à expliquer les conflits sociaux dans le cadre de l’élaboration des politiques publiques, la théorie culturelle du risque soutient que les différences quant à la perception du risque sont interprétables à la lumière du mode d’organisation et de fonctionnement des groupes d’acteurs en présence, soit leurs cultures politiques. 
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\nPour tenter d’éclairer les problèmes liés au processus de développement des dossiers électroniques et de contribuer à l’avancement des initiatives en cybersanté, ce mémoire propose une analyse culturelle de groupes d’acteurs impliqués dans la réalisation des politiques publiques dans le secteur de la santé au Québec. En relevant leur attachement à une culture politique spécifique, nous avons pu déterminer son influence sur la perception du risque des acteurs face au développement et à la gestion des dossiers électroniques des patients dans le système de santé au Québec.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score1.000

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 studies0.0030.007
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.240
Teacher spread0.229 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Qualitative
Domainnot available
GenreEmpirical · Other

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
Published2023
Admission routes1
Has abstractyes

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