Determining the Barriers and Facilitators to Adopting Best Practices in the Management of Poststroke Unilateral Spatial Neglect: Results of a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: A gap exists between best and actual management of poststroke unilateral spatial neglect (USN). Given the negative impact of USN on poststroke recovery, knowledge translation efforts are needed to optimize USN management. To date, no study has investigated the specific barriers and facilitators affecting USN management during the acute care process. OBJECTIVE: To identify the facilitators and barriers that affect evidence-based practice use by occupational therapists (the primary discipline managing USN) when treating individuals with acute poststroke USN. METHODS: Focus group methodology elicited information from 9 acute care occupational therapists. RESULTS: Key barriers identified included lack of basic evidence-based practice skills specific to USN treatment and personal motivation to change current practices and engrained habits. Key facilitators included the presence of a multidisciplinary stroke team, recent graduation, and an environment with access to learning time and resources. Synthesized Web-based learning was also seen as important to uptake of best practices. CONCLUSION: It is estimated that upwards of 40% of patients experience poststroke USN in the acute phase, and we have evidence of poor early management. This study identified several modifiable factors that prepare the ground for the creation and testing of a multimodal knowledge translation intervention aimed at improving clinicians' best practice management of poststroke USN.
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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 | 0.045 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".