Patients' and caregivers' self-perceived stroke education needs in inpatient rehabilitation
Bibliographic record
Abstract
Background: Identifying patients' and caregivers' learning needs and perspectives on provided education activities is vital for the development of effective education in rehabilitation, yet it may not be deemed important. This study explored the stroke education perspectives in a Canadian rehabilitation centre to illustrate one approach for addressing this problem. Methods: This qualitative description study was overlaid by phenomenology; in-depth interviews were transcribed then analysed using a foundational thematic analysis approach. The concepts of reflexivity and credibility were employed to enhance trustworthiness. Findings: Three patients and three caregivers were interviewed. Conveyed education focuses included secondary prevention, rate of recovery, knowledge collection, adherence to home programmes, transition to home, and personal responses to caregiving. Client-centred education, including providing personally relevant exercises, facilitating interaction with other patients, and incorporating print materials, trial discharges, and technology, empowered patients and caregivers to benefit from ‘teachable moments’. Conclusions: If education programmes are to achieve expected outcomes, input from targeted learners about their learning needs and the effectiveness of received education programmes is essential. Qualitative description is one approach for gathering information which can yield valuable insights for those designing education programmes and contribute to improved patient-centred care and rehabilitation outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| 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".