Survey of Stroke Caregiver Training provided by OT, PT, and SLP across Practice Settings
Bibliographic record
Abstract
Aims: To learn how occupational therapists (OTs), physical therapists (PTs), and speech language pathologists (SLPs) in the United States perceive their ability to address the needs of caregivers of stroke survivors and the factors that impact the provision of effective training. Methods: A quantitative exploratory survey method was used. Surveys were mailed to therapists (1,000 per discipline) with 594 returned (OT = 216; PT = 219; SLP = 160). Descriptive data were analyzed for areas related to training that should be targeted for innovative programming. Results: Findings revealed that a variety of methods and structure were used to provide training in traditional role-related areas. Factors, which emerged that impacted caregiver training, were related to perceived caregiver attributes, coordination across the healthcare continuum, topics covered during training, and follow-up training postdischarge. Conclusions: Therapists must coordinate efforts to address needs of caregivers and advocate for the creation of best practices in caregiver training programs that address Affordable Care Act provisions and enable caregivers and stroke survivors to live well with a better quality of life.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".