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Record W4409336958 · doi:10.5334/ijic.icic24514

The ‘recipe for success’: Co-designing strategies to enhance care transitions from hospital to home

2025· article· en· W4409336958 on OpenAlexaboutno aff
Jacobi Elliott, Anna Kras‐Dupuis, Miguel Henrique Pereira de Paiva, Rebecca Cantor, Ashley Camden, Erin Watson

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsRecipeNursingMedicine

Abstract

fetched live from OpenAlex

Background: There is growing recognition of the importance and benefits of patient-and caregiver-centred care approaches for older adults and individuals with complex medical needs during points of care transitions. Effective care transitions can improve patient, caregiver, and provider experiences, and reduce avoidable emergency department visits and readmissions. Despite the widely recognized benefit of effective care transitions, health care programs have struggled with how best to design, implement, and sustain strategies to improve transitions of care for complex individuals. In 2019, St. Joseph’s Health Care London (Ontario, Canada), was awarded funds to improve the quality of care and patient and caregiver experience during transitions through the implementation of transition strategies. A number of interviews and focus groups were conducted to understand current experiences, and identify areas for improvement. Additionally, best practice transition strategies were adapted from Healthcare Excellence Canada. A working group was established with frontline and leadership representation, as well as two patient/caregiver representatives to oversee the work. Co-Design Approach: Through multiple co-design sessions, the following tools and strategies were co-developed and/or modified for implementation: i) patient orientated discharge summary; ii) teach-back education processes; iii) post discharge follow-up phone calls; iv) welcoming caregivers as partners in care, and v) care resource binder. Many of these strategies were mid-implementation at the onset of the pandemic and lost momentum due to system pressures and shifting priorities. In Summer 2022, the working group re-assembled to re-ignite this work with a campaign to generate awareness and get frontline staff excited for the work to come. The team developed a campaign, Recipe for Success, which highlights a recipe card with a description of the ‘ingredients’ (transition strategies) needed for a successful transition. Virtual sessions, co-presented with patient/caregiver partners, were held to raise awareness and provide foundational knowledge on the tools and strategies. Over 100 participants attended the webinar sessions to learn more about the work. Tools were implemented into practice across the rehabilitation and geriatric inpatient programs. Follow up information has been collected to capture experiences and outcomes. Early findings indicate an improvement in care transition experiences. Discussion and Next Steps: A number of key learnings have emerged from this work related to the development and implementation of transition strategies. Input from patients, caregivers and providers – from the beginning, clear communication, a strong workplan, and multiple training sessions were critical for the success of the initiative. The team is actively spreading this work to other units and programs across the organization.

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)0.044
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.056
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0060.007
Scholarly communication0.0090.008
Open science0.0040.012
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.018
GPT teacher head0.436
Teacher spread0.418 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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