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Record W4409337342 · doi:10.5334/ijic.icic24519

Exploring midwifery engagement and collaboration within integrative care models in Ontario, Canada

2025· article· en· W4409337342 on OpenAlexaboutno aff
Angela Freeman, Sherry Espin, Sue Bookey‐Bassett

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careNursingMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Problem Statement: Since 2019, Ontario Health Teams (OHTs) have been forming to coordinate and streamline care across health sectors within the province of Ontario, Canada, but little is known about how midwives and their practice groups are engaging and collaborating within this model of care delivery. Background: In Ontario, midwives provide primary care for pregnancy, birth and postpartum, and are most commonly organized in midwifery practice groups as autonomous providers. From the literature we understand that factors such as funding, care model, and philosophical differences may pose challenges for midwifery integration and collaboration between health professions, and within team-based care models. The aim of this inquiry is to identify what is known about the engagement of midwives and their practice groups within OHTs. Additionally, by highlighting potential facilitators and challenges for midwifery collaboration within integrative care models, this study further aims to develop recommendations to improve engagement and inform future research directions. Who is it for? Findings from this inquiry describe facilitators and challenges for midwifery engagement within integrative care models, as well as recommendations, that are relevant for policy makers, healthcare organizations and primary care providers. These findings will also provide a foundation for future research and engagement with midwives, their organizations, collaborators and leaders within OHTs. What did you do? A rapid review of the literature and environmental scan related to midwifery partnering and collaborating within cross-sectoral integrative partnerships was undertaken. More specifically, we reviewed the literature to identify where and how midwives are engaging and collaborating within integrative care models, and identify facilitators and challenges to this collaboration. A review of OHT websites and public-facing documents was additionally undertaken to describe to what extent, and in what capacity, midwives and practice groups are engaging within OHTs. What results did you get? What impact did you have? Results from this preliminary inquiry provide context and a foundation for how midwifery professionals are engaged within integrative care models as collaborators within OHTs provincially. Findings from the literature identify facilitators and challenges to the collaboration by this professional group. In addition, results inform research questions and focus an approach for further exploration of the current state of midwifery engagement and collaboration within healthcare coordination at the OHT level. What is the learning for the international audience? While Ontario is working toward building integrative care models that include primary care providers, we anticipate that engagement and collaboration of midwifery practices and professionals within OHTs will vary across the province. Findings from this study may also offer insights regarding the engagement of other specialized services within OHTs. What are the next steps? The overall aim of this study is to develop recommendations to support the engagement and collaboration of midwifery professionals within integrative models, such as OHTs, and to identify areas for further inquiry.

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)0.014
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.261
Threshold uncertainty score0.857

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0200.007
Scholarly communication0.0070.004
Open science0.0030.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.395
GPT teacher head0.544
Teacher spread0.149 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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