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Record W4392955537 · doi:10.1177/11786329241237709

Inter-Establishment Complex Musculoskeletal Care Pathways in Montreal: Timeline of a Collaboration Involving a Research Team Within a Continuous Quality Improvement Initiative

2024· article· en· W4392955537 on OpenAlexaffabout
Marie Beauséjour, Martin Sasseville, Aurélie Vigné, Sophie Riendeau, Stephanie L. Gould, Kelly Thorstad

Bibliographic record

VenueHealth Services Insights · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsShriners Hospitals for Children - CanadaHôpital Charles-Le MoyneCentre Hospitalier Universitaire Sainte-JustineUniversité de Sherbrooke
Fundersnot available
KeywordsTimelineAuditProcess managementContext (archaeology)Knowledge managementQuality managementProject teamHealth careQuality (philosophy)Process (computing)Key (lock)Service (business)MedicineBusinessComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Children and adolescents with complex musculoskeletal conditions may receive health care that requires at least 1 transfer between 4 specialized pediatric establishments in the Montreal region (Québec, Canada). This may result in challenges in navigating the system. A collaborative approach, aiming to make the inter-establishment care pathways seamless and to improve the integration of musculoskeletal health services, brought together key stakeholders including a research team. The aim of this paper is to describe the timeline of the collaborative approach's key milestones and activities and, more specifically, to describe the context, process, and outputs of the involvement of researchers in support of a continuous quality improvement project based on an integrated approach. The descriptive timeline was constructed from a qualitative document analysis of the project-related gray literature (n = 80 documents) and was validated and interpreted with key stakeholders. The results showed how the collaborative project was set up and operated, as well as what solutions were developed and implemented. The strategies on how the research team was involved in the integrated approach in addition to its research activities were also described. Conclusions suggest practice recommendations for creating change processes by integrating research, service evaluation and clinical audit into quality improvement projects.

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)0.000
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 score0.205
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.491
Teacher spread0.408 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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