Patients and Care Providers Translating Care Transition, Integration, Coordination and Continuity Practices into Real Experiences
Bibliographic record
Abstract
Background: The literature confirms the struggle healthcare service providers and recipients have with clearly describing perspectives, experiences, and outcomes of patients/families (P/F) and care providers (HCPs) regarding care transitions, and particularly as aligned with care integration, coordination and continuity. Most studies focused only on P/F experiences framed around patient or person-centred care. Most measures were developed by multidisciplinary teams including researchers and clinicians but void of P/Fs. Most did not capture parallel experiences of HCPs.How aligned or divergent are P/F and HCP care transition perceptions and experience with those regarding care integration, coordination, and continuity? Unless P/Fs and HCPs co-design their own care experience measures, how will we really understand what matters to them? Our study aimed to improve understanding of the P/F and HCP perspectives and experiences through: () co-designing with P/Fs and HCPs key experience domains and measures that mattered to them regarding successful care transitions including care integration, coordination and continuity practices and policies; (2) identifying what worked well and what needed improving to achieve successful patient transitions across settings; and (3) sharing findings to inform quality improvement strategies for care transitions with consideration for the pillars, principles, policies and practices of care integration, coordination and continuity. Approach: Between 208 and 2024, broad recruitment strategies were employed across acute and community-based settings in the five zones within Alberta Health Services with the intent of having multiple self-selected settings participate in building their capacity to co-design and implement a care transitions quality improvement strategy. Specifically, the request was for care settings to () establish a team of HCPs, quality improvement staff and P/F advisors; (2) learn to co-design P/F and HCP care transition experience measurement surveys/tools that included exploring integration, coordination and continuity of care; and (3) gather and analyze data to inform care transition practices, policies and system factors needing improvement generally, and specifically as aligned with integration, coordination and continuity. Results: Thirty-two care teams involving 79 staff and HCPs, and 26 P/F advisors agreed to participate in the study, co-designing relevant P/F and HCP care transition measurement tools that included some measures from the literature and what they felt mattered to them regarding integration, coordination, and continuity of care. P/F advisors gathered ,038 P/F survey responses and 40 care staff/HCPs completed online surveys. The aggregated findings guided the development of a set of care transition domains and measures, inclusive of relevant integration, coordination, and continuity care. Findings guiding our understanding of the experiences of P/Fs and HCPs regarding what worked well, and where there were issues, were also used to inform quality and safety improvement initiatives for each setting. Study findings were shared with key stakeholders and leaders. Implication: P/Fs and HCPs co-designing what matters to them regarding care transition perspectives and experiences enhanced our understanding of what and how these measures aligned with those regarding care integration, coordination, and continuity. Exploring and describing one of these care types includes consideration of the others. Together they more appropriately inform quality care and areas of improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".