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Record W4390945363 · doi:10.5334/ijic.icic23282

Workforce Capacity Building: Strategies for Interprofessional Education in Integrated Care

2023· article· en· W4390945363 on OpenAlexaffabout
Sue Bookey‐Bassett, Sherry Espin

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIntegrated careThematic analysisWorkforceHealth careNursingInterprofessional educationWorkforce developmentWork (physics)Medical educationQualitative researchPsychologyMedicineSociologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

Introduction: Integrated health services are recommended as an approach to manage and deliver person-centred care across different sectors throughout a person's lifespan (WHO, 2015). Training future and current health care professionals to work in integrated care systems requires additional knowledge, skills, and competencies. Interprofessional education (IPE) is recommended as a critical strategy in preparing the healthcare workforce for more integrated service delivery models (Bookey-Bassett et al., 2022). However, it is unclear what strategies are being used to implement IPE to support the development of the current workforce for integrated care, in Ontario hospital to home programs. Methods: This study explored the perceptions and experiences of key informants regarding how IPE is utilized in training current healthcare professionals to work in hospital to home integrated care programs in Ontario, Canada. A qualitative descriptive design was utilized. Key informants included 15 leaders from 13 integrated care programs representing varied healthcare settings across the province. Individual interviews were conducted, audio-recorded, and transcribed. Data analysis followed a thematic analysis approach (Braun & Clarke, 2006). Findings were elucidated through the lens of the interprofessional learning continuum model (Institute of Medicine, 2015) and current competencies for integrated care (Langins and Borgermans, 2015). Key Findings: Thematic analysis revealed key themes reflecting participants’ understanding and experiences of IPE within their specific hospital to home integrated care programs; participants’ perceptions of informal and formal strategies to implement IPE within integrated care programs; current barriers and facilitators to implementing IPE in hospital to home integrated care programs; and recommendations for interprofessional and intersectoral collaboration to support workforce capacity and capability related to clinical and professional integration. Conclusions: As new models of hospital to home integrated care are rolled out, IPE should be incorporated as part of the implementation process. Further, IPE should be context specific and adapted to meet the changing needs of patient populations, changing roles of health care providers, and integrated care frameworks. Implications and next steps: Study findings provide important implications for health professions’ education including formal and informal continuing education. Findings are also relevant for leaders implementing integrated care programs across various practice settings. Next steps include consultation with practice partners and patient partners to co-design and pilot test an IPE intervention within a hospital to home integrated care program for surgical patients.

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)0.030
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0130.011
Scholarly communication0.0120.014
Open science0.0050.038
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0140.002

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 head0.039
GPT teacher head0.451
Teacher spread0.412 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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