Implementing a Patient and Family Experience Questionnaire in a Regional Stroke Prevention Clinic : Outcomes and Lessons Learned
Bibliographic record
Abstract
Background The Toronto Stroke Networks worked closely with stroke survivors and their families to co-design a novel patient and family experience questionnaire (PFEQ) to reflect experience across the stroke system of care. The PFEQ provides a deeper understanding of emotional experiences compared to generic satisfaction surveys. In order to capture overall stroke-specific experiences across the continuum of care, this project aimed to implement and evaluate the administration of the PFEQ in a regional stroke prevention clinic (SPC).MethodImplementation was co-designed with six SPC staff. Feedback loops supported an iterative the process for administering the PFEQ to stroke patient/family members. Using thematic analysis, qualitative themes related to lessons learned and patient experiences will be abstracted from staff interviews and completed questionnaires.ResultsSPC staff co-developed the following principles for implementation: 1) administration of the PFEQ by administrative staff when possible; 2) discussion of the PFEQ with stroke patients/family members during their appointment with the SPC nurse; and 3) ensuring that the administration process is iterative and aligns with SPC processes. Within the first two weeks, 20 PFEQs have been completed with no negative feedback from patients, family members or staff. Results of thematic analysis are pending. Conclusions A collaborative and iterative implementation approach allowed for timely uptake and use of a PFEQ in a regional SPC. Further analyses from interviews will identify additional recommendations to support sustainability and spread of the PFEQ to other stroke clinics. Results from the PFEQ will be used to inform stroke system planning
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".