Addressing post-stroke cognitive impairment through innovative application of health psychology principles
Bibliographic record
Abstract
Background: Cognitive impairment (CI) is a pervasive outcome of ischaemic stroke, with implications for stroke recovery, medication and rehabilitation adherence, and progression to dementia. Intervention for post-stroke cognitive impairment has received considerably less attention than rehabilitation for physical deficits. Through a series of studies conducted in the Republic of Ireland, the prevalence of post-stroke cognitive impairment and the absence of appropriate rehabilitation have been identified. Methods: Two national audits of acute and community stroke care (2008 and 2015) and a cohort study (ASPIRE-S) of 256 patients with acute ischaemic stroke followed up at 6 months (2011-2012) and being recalled at 5 years (2016-2017) will be described. Methods used include analysis of national hospital discharge data, qualitative interviews with healthcare professionals and surveys of patients with stroke and their carers. Findings: Cognitive impairment is reported in over half of patients six months post-stroke. It is identified as a common stroke outcome in national audits, with rehabilitation provision minimal to non-existent. While over 90% of Irish stroke patients interact with a stroke specialist nurse and 81% receive physiotherapy, 1.6% receive input from psychological services, with no recorded indication of rehabilitation provided for cognitive difficulties. Discussion: Findings of studies conducted in Ireland to date highlight a substantial unmet need for rehabilitation for cognitive impairment post-stroke. This issue is not unique to Ireland. These findings have led to the establishment of a research programme - the StrokeCog study - to develop an intervention for post-stroke cognitive impairment using principles of behavior change theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".