Sexuality after a stroke: co-designing and pilot testing an evidence-based knowledge translation intervention to improve rehabilitation services
Bibliographic record
Abstract
PURPOSE: To co-design and pilot test a theory-driven knowledge translation (KT) intervention with various key stakeholders involved in stroke rehabilitation to improve the provision of sexual rehabilitation services. METHODS: This study was conducted using qualitative methods for the co-design of the KT intervention, and a longitudinal quantitative design for its pilot implementation. The KT intervention was codesigned during five online workshops with individuals with stroke, partners, clinicians, managers and researchers. Data collection and analysis were based on the Theoretical Domains Framework. One inpatient stroke rehabilitation center implemented the KT intervention for 19 months. Monthly audits were conducted 6 months before and for each month of implementation of the KT intervention. Quantitative data were analyzed using descriptive statistics. RESULTS: The codesign led to the development of a multifaceted KT intervention aiming to influence nine determinants of behaviors. The pilot implementation of the KT intervention improved the proportion of patients who received sexuality-related services during their rehabilitation from 33% (5/15 patients; baseline) to 74% (113/152 patients), and at least 56% received education regarding sexuality. CONCLUSIONS: The pilot implementation of this evidence-based multifaceted KT intervention suggests the intervention is applicable and can influence the provision of guideline-concordant sexual rehabilitation services for stroke patients.
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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 | 0.043 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".