Development, Implementation, and Clinician Adherence to a Standardized Assessment Toolkit for Sensorimotor Rehabilitation after Stroke
Bibliographic record
Abstract
Purpose: This study describes the development of a standardized assessment toolkit (SAT) and associated clinical database focusing on sensorimotor rehabilitation in three stroke rehabilitation units (SRUs). Implementation of the SAT was confirmed using objective measures of clinician adherence while exploring reasons for varied adherence. Method: Participants were patients post-stroke admitted for inpatient rehabilitation and clinicians from the three SRUs. A collaborative and iterative process was used to develop the SAT. Implementation was measured by clinician adherence, which was charted by means of assessment entries in patient records and transferred to the clinical database. Reasons for lower adherence were interpreted from therapist data logs at one SRU. Results: The SAT consisted of 25 assessment tools. Clinician adherence to a subset of the tools ranged from 33% to 99% at admission and from 28% to 94% at discharge. At one site, lower adherence among the tools was explained by patient-related factors (1%–36%) and protocol or logistical reasons (0%–7%) at admission; missing data ranged from 0% to 3%, except for the Montreal Cognitive Assessment (17%). Conclusions: In this pragmatic study, objective measures of clinician adherence demonstrated the feasibility of implementing an SAT in daily practice. Moreover, the reasons for lower adherence rates may be related to the patients, protocol, and logistics, all of which may vary with the assessment tool, rather than clinician compliance.
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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 | 0.080 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".