Recovery-oriented Care : Supporting The Patient Experience After Stroke Starts With Hope
Bibliographic record
Abstract
BackgroundAlthough improving patient experience is a recognized priority, stakeholders within the Toronto Stroke Networks (TSNs) identified that optimal psychosocial care including the use of hopeful language are lacking. Literature suggests that attending to psychosocial needs and nurturing hope is equally as important as biomedical care and contributes to positive health outcomes. In practice, perceptions about hope vary, and hope is often u2018managedu2019 for fear it will lead to unrealistic recovery expectations. Psychosocial care falls to select professions, leaving others ill-equipped to address issues. The TSNs identified the need for a core level of competency for all healthcare providers (HCPs) including clarifying roles, adapting behaviours and optimizing interprofessional communication and relationships with patients to nurture hope and recovery.MethodologyA literature review and environmental scan examining the impact of psychosocial care and promoting hope in stroke recovery were completed. Persons with stroke/caregivers shared their experience of hopeful and psychosocial care. HCPs were interviewed to ascertain their needs from the system to optimize hope-inspiring psychosocial care. ResultsA novel Psychosocial Care Model for Stroke was developed incorporating results from our methodology. The model reflects core process and communication elements and core competencies such as communication style, counselling skills, self-awareness around hope and biases, etc. This model will guide HCP educational/knowledge transfer program development.ConclusionAttending to psychosocial issues and integrating hope in delivering interprofessional post-stroke care represents a shift in the delivery of stroke care in Toronto. Optimizing HCP competency to improve recovery-oriented care has promise for better patient functional outcomes and HCP job satisfaction
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".