MétaCan
Menu
Back to cohort
Record W4401954895 · doi:10.1177/08404704241264021

Enhancing interprofessional collaboration for unattached patients in primary care

2024· article· en· W4401954895 on OpenAlexafffundabout
Élise Boulanger, Maxine Dumas-Pilon, Victoria Wicks, Isabelle Gaboury, Mylaine Breton, Mélanie-Ann Smithman

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsUniversity of TorontoUniversité de SherbrookeMcGill UniversityClinique Paro Excellence
FundersCollege of Family Physicians of Canada
KeywordsPrimary careNursingMedicineHealth careHealth professionalsPrimary health careInterprofessional educationFamily medicinePolitical science

Abstract

fetched live from OpenAlex

This project explored an interprofessional collaboration initiative at Clinique Indigo which aimed to improve comprehensive care for unattached patients in Quebec's primary care system. Throughout the project, physicians and non-physician health professionals alike became more actively engaged in the care of patients lacking a regular primary care provider. The project successfully demonstrated that defining a common vision for "well care" within the clinic and integrating diverse professionals could significantly improve quality of care for unattached patients, evidenced by an increase from 13% to 43% in comprehensive care provision. However, the initiative also faced challenges, including professional turnover and gaps in primary care training, suggesting critical areas for future improvement in healthcare policy and practice. These results support expanded interprofessional approaches in primary care to address systemic care disparities in universal healthcare settings such as this one caused by the differential or absence of attachment to a primary care provider.

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)0.015
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0180.004
Scholarly communication0.0060.003
Open science0.0030.021
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.013
GPT teacher head0.346
Teacher spread0.333 · 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.

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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicHealth Promotion and Cardiovascular PreventionFrench-language works237,207