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Record W2341941870 · doi:10.13162/hro-ors.v4i1.2689

Augmenter l’accessibilité et la qualité des services de santé de première ligne avec les Groupes de médecine de famille

2016· article· fr· W2341941870 on OpenAlexaffvenue
Émélie L. Aubin, Amélie Quesnel‐Vallée

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2016
Typearticle
Languagefr
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Pour faire suite aux recommandations de la Commission Clair en 2000, les premiers groupes de mdecine de famille (GMF) ont t implants au Qubec en 2002. Cette rforme avait pour but d'amliorer l'accs et la qualit des services de premire ligne, reconnus comme dficients. Afin d'y parvenir, les GMF devaient tablir une pratique interprofessionnelle et multidisciplinaire offrant des heures et jours de services tendus, notamment grce au partage des patients par un groupe de mdecins qui collaborent avec des adjoints administratifs, du personnel infirmier et autres professionnels de la sant, en plus de l'informatisation des dossiers lectroniques de patients. Cette rforme rencontrait l'assentiment de nombreux acteurs (mdecins, personnel infirmier, population gnrale) qui anticipaient tous en bnficier divers gards. Cependant, la mise en oeuvre prcipite de cette rforme a caus beaucoup de confusion et requis de nombreux rajustements, dont certains sont encore venir. De fait, ce jour, la rforme a eu moins d'impacts qu'anticips, le nombre de patients inscrits aux GMF ayant peine augment et l'informatisation tardant se faire. Enfin, bien que les patients inscrits en GMF peroivent une plus grande continuit des soins dans le cadre de cette pratique interprofessionnelle, ils ne peroivent cependant pas d'amlioration en ce qui concerne l'accs aux soins.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.400
Teacher spread0.345 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2016
Admission routes2
Has abstractyes

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