Integrating Care from Home to Hospital to Home: Using Participatory Design to Develop a Provincial Transitions in Care Guideline
Bibliographic record
Abstract
Introduction: Patients worldwide experience fragmented and uncoordinated care as they transition between primary and acute care. To improve system integration and outcomes for patients, in 2017/2018 Alberta Health Services (largest health services delivery organization in Canada) called for a coordinated approach to improve transitions in care (TiC). Healthcare leadership responded by initiating the development of a province-wide guideline outlining core components of effective transitions in care. This case study highlights the extensive design process used to develop this guideline, with a focus on the participatory design (PD) approach used throughout. Methods: An iterative, mixed methods PD approach was used to engage over 750 stakeholders through the following activities to establish Guideline content: i) learning collaborative; ii) design-team; iii) targeted online surveys; iv) primary care stakeholder consultation; v) modified Delphi panel; and vi) patient advisory committee. Results: The result was Alberta's first guideline for supporting patients through TiC: "Alberta's Home to Hospital to Home Transitions Guideline". Conclusion: The extensive design process used to create the Guideline was instrumental in establishing content, encouraging system integration, and creating conditions to support provincial implementation. While intended to improve and standardize patient care in Alberta, the methods used and lessons learned throughout the development of the Guideline are applicable internationally.
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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 | 0.096 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".