Hope Coaching : Empowering Patients To Enhance Their Experience of Stroke Rehabilitation
Bibliographic record
Abstract
Hope has been highlighted by the Toronto Stroke Network as an empowering tool for patient engagement to impact therapy (Rubin, 2017). Rehabilitation nursing in particular is noted as having specialized skills for the u201cmaintenance of hopeu201d (Routsalo, Arve & Lauri, 2004, p.209). Despite this finding, there remains a challenge for patient engagement in stroke rehabilitation nursing detected from a quality improvement investigation at Providence Healthcare, a rehabilitation facility in Toronto, Ontario. The challenge is that some patients do not view nursing as part of the u2018therapyu2019 plan and exhibit less engagement working with nurses in rehabilitation. To address this gap, Providence Healthcare piloted a program called u201cHope Coachingu201d delivered by Social Workers trained in Solution-Focused Brief Therapy (SFBT). Nurses and Interprofessional teams are trained in an SFBT, strengths-based approach with the goal of building greater therapeutic rapport with patients to provide motivation for stroke rehabilitation. Self-report, post-training surveys have found that staff now know what to say to help provide hopeful care. Patient feedback was unsolicited and expressed at a team-family meeting to highlight the benefits of a hope approach to build rapport. Next steps involve training more Interprofessional teams on the stroke rehabilitation units to determine the impact of hopeful patient care.ReferencesRoutasalo, P., Arve, S., & Lauri, S. (2004). Geriatric rehabilitation nursing: Developing a model. International Journal of Nursing Practice, 10. 207-215. doi: 10.1111/j.1440-172X.2004.00480.x Rubin, A. (2017). An environmental scan examining the evidence supporting psychosocial care and the adoption of hopeful care in stroke recovery. Toronto Stroke Networks. 1-30. [PDF document]. http://www.avivarubin.com/
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".