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Record W4390944926 · doi:10.5334/ijic.icic23320

Examining the Care Transition Experiences of Home Care Clients Using Journey Mapping

2023· article· en· W4390944926 on OpenAlexaffabout
Kerry Kuluski, Sandra McKay, Sonia Nizzer, Marianne Saragosa

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSinai Health SystemCARE CanadaUniversity of TorontoTrillium Health Centre
Fundersnot available
KeywordsNursingHealth careAgency (philosophy)Qualitative researchMedicineGovernment (linguistics)

Abstract

fetched live from OpenAlex

As a priority area of the Canadian government, home care is heavily relied on to support an integrated, accessible, and sustainable health system. VHA Home Healthcare is a large home care agency in Ontario, Canada. Home care provides services that span personal support, nursing, physical and occupational therapy and more to assist people in remaining in their homes and community. Although home care is associated with delayed and reduced hospital use, some home care recipients will require hospital admission. Unplanned hospital admissions in Ontario account for CAD 1.2 billion in healthcare spending. However, to date, little attention has been paid to the unique needs of clients receiving home care and experiencing a transition to and from the hospital. The proposed study aimed to answer the following research questions: 1)What is the experience of home care clients transitioning from home to hospital and back home and having their home care services resumed? 2)What are opportunities and recommendations to better support home care clients transitioning to and from the hospital? Our study used a descriptive qualitative design by conducting individual interviews and applying a patient journey mapping approach. Patient journey mapping involved identifying the clients’ experience, their touch points across the system, pain points and facilitators. A total of 7 clients and caregivers participated in 12 interviews across two phases. We analyzed the data using qualitative content analysis, and aggregated findings visually depicted the journey on a map. Our results demonstrated that participants experienced hospital admissions as challenging, whether planned or unplanned. Pain points involved having an adverse outcome while in the hospital, including missed or delayed care, attempting to seek additional information to fill knowledge gaps, and sensing not being listened to. Once discharged, clients reported managing multiple service providers and agencies for exacerbated care needs and receiving inconsistent and limited information when in the community. Participants described the system and individual facilitators that mitigated the pain points. These involved having consistent and supportive coordinators and providers in the community before and after the discharge home, having a discharge plan, and accessing an interprofessional team when hospitalized. At a personal level, many participants relied on past professional and personal experience when advocating and navigating the health care system. Self-advocacy skills were essential to ensure that participants had a quality care transition. Our study has several important implications. Notably, patient journey mapping is an effective tool to diagram the home care client journey. Findings also indicate that clients and caregivers often mitigate poor outcomes using advocacy skills and personal and professional experiences. To this end, we can learn from client journeys and leverage clients’ lived experiences to design interventions targeting pain points during care transitions.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.471
GPT teacher head0.598
Teacher spread0.127 · 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
Published2023
Admission routes2
Has abstractyes

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