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Record W4409337212 · doi:10.5334/ijic.icic24193

“There’s No Room for Silos.” Interprofessional Education in Hospital-to-Home Integrated Care Programs.

2025· article· en· W4409337212 on OpenAlexaboutno aff
Sue Bookey‐Bassett

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsNursingInterprofessional educationMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Introduction: Preparing current and future health care providers to work in integrated care models requires interprofessional learning about working in teams across health sectors and integrated care concepts/principles. Evidence indicates that Interprofessional education (IPE) is essential for training health and social care providers and building workforce capacity for new models of integrated care. Yet how we are preparing current and future health care professionals (HCPs) to work in these models of care is unclear. Therefore, we sought to understand how IPE is implemented in existing hospital-to- home integrated care. We report key informants’ descriptions of IPE in training existing HCPs to work in hospital-to-home integrated care programs in Ontario Canada. Method: Utilizing a qualitative descriptive approach, interviews were conducted with 15 leaders of hospital-to-home integrated care programs across the province. Interviews were audio-recorded and transcribed verbatim. Data analysis employed a thematic analysis approach. Findings were interpreted through the lens of an interprofessional learning continuum model (Institute of Medicine, 2015) and competencies for integrated care (Langins and Borgermans, 2015). Findings: Formal and informal IPE through staff orientation and team processes within the integrated care programs can support competency development (e.g., role clarity, communication, and teamwork) for interprofessional practice within hospital-to-home integrated care programs. Key informants acknowledged the importance of cross sector IPE to understand patient care trajectories and provider roles more fully. Conclusions: The findings provide examples of the need for both formal and informal IPE in these hospital-to-home integrated care programs. Interprofessional teamwork, learning together, and having no room for silos reinforced the importance of continuing interprofessional learning for existing HCPs in the context of hospital-to-home integrated care programs. IPE in integrated care programs is required to meet the changing needs of patient populations, shifting roles of health care providers, and evolving health care systems. Implications for Education and Practice: This work has direct implications for preparing current and future health care professionals to work in new models of integrated care such as hospital-to-home programs where collaborative approaches are critical to support safe, quality patient care within and across health and social care sectors. Education content should include concepts and principles related to IPE, collaborative teamwork, and fundamentals of integrated care. Training should begin in formal academic programs and continue in practice settings. Student placements for health professionals should be considered as a mechanism to develop knowledge and competencies for integrated care. Cross-sector training can help health and social care providers understand the focus of the integrated care program (e.g., patient pathways, referrals) and the roles and responsibilities of various team members. Next steps: We are currently engaging academic and practice leaders to explore the feasibility of creating new nursing student placement opportunities within hospital to home integrated care programs with the aim of building knowledge and competencies for integrated care.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.344
Threshold uncertainty score0.683

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.006
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.412
Teacher spread0.402 · 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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