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Record W4390945154 · doi:10.5334/ijic.icic23059

Understanding and Managing Fragmented Transitions in Care: Learning from and Guided by Patient, Family, Health Care Provider and System-Level Experiences Before, During and Follow-up to COVID-19

2023· article· en· W4390945154 on OpenAlexaffabout
Katharina Kovacs Burns, Marian George

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsCanadian Patient Safety InstituteAlberta Health Services
Fundersnot available
KeywordsNursingHealth careAcute careMedicineService providerPsychologyService (business)BusinessPolitical science

Abstract

fetched live from OpenAlex

One ongoing challenge in health care is ensuring patients/families and also health care providers have clear direction and implementation guidance around transitions in care across all care and community settings that includes integration, continuity and coordination. Clearly understanding patient, family, care provider and system-level experiences including what works and where improvements are needed with care transitions across acute and community settings has been an ongoing challenge for most health care systems before and during COVID-19. However, although efforts to identify and use experience measures were complicated and impacted during COVID-19 because of rapidly enforced restrictions and emergent safety protocols, learning to adapt to change was also essential. Our study explored the experiences of patients/clients, families, care providers and system leaders regarding their care transitions between acute and community-based care settings prior to and during COVID-19, along with changes in care outcomes, practices, policies and services became the focus of a two-year pilot study in one Canadian provincial health system. We co-designed relevant acute to community care transition process and outcome/impact experience indicators/measures with patients/clients, families and care providers; and explored the feasibility for transferring measures and lessons learned for practice, policy and service changes as part of follow-up and post COVID new ‘norm’ transformation of care transitions. The study involved the Provincial Seniors and Continuing Care Advisory Council, Continuing Care Quality Committee, Transition Services, primary care and eight pilot care settings involving patients/clients transitioning from acute to community settings. Each care setting involved patient/family advisors co-designing and implementing the initiative with care providers and transition leaders. This included survey development, and gathering, analyzing and interpreting client/patient and care provider experiences. Findings in each and across settings included identifying common core patient/family and care provider experience indicators/measures regarding acute to community care transitions, before and during COVID. System-level factors and experiences were also gathered. Themes for what makes transitions in care successful before, during and follow-up to COVID were also confirmed – e.g. clear communication, navigation and information/direction for all stakeholders. The aggregated findings targeted health outcomes and guided changes in transition practices from across acute points of care as well as Emergency, to various community-based care settings including Home Care, Long-term or other interim programs utilized while patients waited for final transition decisions – e.g. CHOICE programs. Essential learnings include having a clear understanding of the experiences of patients/clients, families, care providers and system-level regarding care transitions, and how well integrated, coordinated and continuity-aligned this care is. This includes understanding what works well and where there are gaps in the system. Managing the gaps helps mitigate failed or unsatisfactory patient transitions across care settings. Such findings also guide or inform quality and safety improvement. Identified transition core measures continue to be studied beyond the pilots for transferability across acute, emergency, primary and community care settings. As well, COVID-19 impacts on practice, policy and service changes involving coordinated and integrated transitions need to be monitored for how well care settings adapt to “new norms” and meet patient/client needs. This work continues.

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)0.024
metaresearch head score (Gemma)0.038
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 score0.082
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.010
Scholarly communication0.0090.007
Open science0.0050.019
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0020.001

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 head0.089
GPT teacher head0.396
Teacher spread0.307 · 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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