Let's Talk About Sex! - Improving sexual health for patients in stroke rehabilitation
Bibliographic record
Abstract
Sexual health contributes greatly to quality of life. Research shows that stroke survivors want to learn and talk about sexual health, but are not given information. In keeping with the Canadian Best Practice Recommendations for Stroke Care, this project aimed to provide all stroke rehabilitation inpatients with the opportunity to discuss sexual health concerns with healthcare providers at West Park Healthcare Centre, a rehabilitation and complex continuing care centre in Toronto. Gap analysis conducted via staff member interviews and retrospective chart reviews showed that close to no patients were given the opportunity to discuss sexual health concerns at baseline. Plan-Do-Study-Act (PDSA) methodology was used as the project framework. The changes implemented included a reminder system, standardization of care processes for sexual health, patient-centred time points for the delivery of sexual health discussions, and the development of a sexual health supported conversation tool for patients with aphasia. By the end of the ten month project period and after three PDSA cycles, the percentage of patients provided with the opportunity to discuss sexual health during inpatient rehabilitation increased to 80%. This quality improvement project successfully implemented the Canadian Best Practice Recommendations for Stroke Care with respect to sexual health. Lessons learned included the importance of early baseline data collection and advance planning for tools used in QI projects. Future projects may focus on improving the discussion of sexual health concerns during outpatient stroke rehabilitation.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".