Implementing the patient partnership approach to quality improvement of care and services: A multiple case study protocol
Bibliographic record
Abstract
CONTEXT: Patient and family partners are being increasingly engaged worldwide in processes aimed at the quality improvement (QI) of healthcare services. There is also growing interest in documenting these engagement processes within organizations to share and improve them. To support the provincial implementation of this approach, the Quebec's ministry of health and social services published, in 2018, the "Framework for the partnership approach between patients, their families and health and social service stakeholders". However, while this framework provides guidelines by describing each partner's role and the ways in which patient and family partners should be engaged in QI processes, it remains unclear how these recommendations were actually used and implemented by different healthcare organizations. The aim of this paper is to present the protocol of a multiple case study that is being conducted to document how this approach was implemented in different large healthcare organizations in Quebec. This study is being conducted in partnership with a patient partner/co-researcher. METHODS: This qualitative multiple case study will be conducted in four large healthcare organizations in Quebec. Twelve to 15 key respondents will be recruited for each case. Data will be collected from multiple sources: 1) semi-structured individual interviews with the key respondents, 2) non-participant observations of the meetings of the QI committee engaging PFPs and 3) analysis of documents describing the implementation context, vision, structures and/or processes. The framework method will be used to conduct intracase and intercase qualitative data analysis. DISCUSSION: The multiple cases included will allow for comparisons between different ways of engaging PFPs in QI processes within an organization, the factors influencing these practices, their advantages and disadvantages, and their implementation outcomes. The conclusions drawn from this study will allow us to make recommendations regarding PFP engagement in the QI of care and services and to propose implementation examples for other organizations wishing to design and implement PFP engagement initiatives in their context in Quebec or elsewhere.
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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 | 0.124 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".