The Language of Recovery: How Effective Communication of Information Is Crucial to Restructuring Post-Stroke Life
Bibliographic record
Abstract
BACKGROUND: Providing appropriate and effective information to people with stroke and their families has been identified as a key component to successful practice. Researchers continue to focus on "lack of information" as being the lack of specific technical medical information rather than the communication of practical knowledge and how people use that knowledge to restructure life after stroke. To meet patients' expectations and achieve better outcomes in stroke, professionals need access to communication theory, research, and training. OBJECTIVES: Improve stroke communication systematically. METHOD: This article will examine stroke communication using a three-part framework: 1. Utilize theory to clearly conceptualize how communication influences stroke outcome. 2. Identify components and mechanisms of communication content to positively influence stroke outcome. 3. Develop goals and strategies for putting content skills into stroke communication practice. CONCLUSION: Relatively little is known about the content and structure of informal communication transactions between stroke survivors, families, and health care professionals and how they accommodate (or resist) realignment of identity after stroke. The professional discourse attempts to ensure realistic expectations of recovery whereas stroke survivors and families complain about the negative discourses, how possibilities for life after stroke are presented, and the hopelessness that this creates. More research is required into how these different discourses affect outcomes.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".