Sensory impairment after stroke: Exploring therapists’ clinical decision making
Bibliographic record
Abstract
BACKGROUND: Stroke survivors experience sensory impairments that significantly limit upper-limb functional use. Lack of clear research-based guidelines about their management exacerbates the uncertainty in occupational therapists' decision making to support these clients. PURPOSE: This study explores occupational therapists' clinical decision making regarding upper-limb, post-stroke sensory impairments that can ultimately inform approaches to support therapists working with such clients. METHOD: Twelve therapists participated in a qualitative descriptive study. Transcripts of semi-structured interviews were analyzed using content analysis. FINDINGS: Three overarching categories were identified: deciding on the focus of interventions (describing intervention choices), it all depends (outlining factors considered when choosing interventions), and managing uncertainty in decision making (describing uncertainty and actions taken to resolve it). IMPLICATIONS: Providing training about post-stroke sensory impairment and decision making may improve therapists' decision making and ultimately improve client outcomes. Further research is needed to understand the impact of uncertainty on occupational therapy decision making and resulting care practices.
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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 | 0.030 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".