Abstract HUP13: Participants With Complex Impairment Profiles Are Systematically Excluded From Stroke Psychosocial Support Research: A Scoping Review
Bibliographic record
Abstract
Introduction: Disability after stroke often requires mental health treatment, but contradictorily, also restricts access to it. People with cognitive, motor, language, and perceptual impairments have been excluded from research on psychosocial supports like psychotherapy, support groups, and meaningful life activities. This exclusion adds to the inequitable distribution of benefits, including fewer treatment options and less knowledgeable providers. Very little is known about who is excluded from stroke psychosocial support research, why they are excluded, and how exclusion occurs. Methods: Exclusionary practices were investigated using a Johanna Briggs Institute-method scoping review. Independent reviewers screened searches of CINAHL, Embase, MEDLINE, and PsychInfo, resulting in 101 included studies. Content analysis was used to analyze studies following an a priori coding framework, which included: Design, inclusion/exclusion criteria, stroke impairment, psychiatric comorbidity, and psychosocial support type. Results: Samples were commonly limited to mild cognitive impairment, with other subpopulations under-represented. Moderate-to-severe cognitive and language impairments were systematically excluded from psychotherapy studies. In contrast, support group and meaningful life activity studies more often included a range of impairments, but also more often excluded psychiatric comorbidity. Studies with rigorous designs rarely included complex impairment profiles. Reasons for exclusion were seldom provided, and participant characteristics were routinely underreported. Conclusion: By including only people with mild and uncomplicated impairment profiles, the field risks neglecting the complex realities of others who arguably have even greater need for support. This scoping review offers recommendations for stroke research and practice, including: The inclusion of a range of stroke impairments in research samples, adapting procedures using compensatory strategies to facilitate inclusion, and greater cross-pollination between mental health and rehabilitation disciplines. This review serves as a call to action to make psychosocial supports equitable and accessible for all people, regardless of ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".