Feeling excluded in financial environments: Challenges and strategies to promote financial inclusion for adults with post-stroke communication disabilities
Bibliographic record
Abstract
• -Communication disabilities after a stroke made financial participation challenging. • -Challenges in financial environments affect the mental and financial well-being of people with stroke-related communication disabilities. • -Most of the challenges of people with stroke-related communication disabilities in financial environments stem from social contexts. • -Inaccessible and non-inclusive financial environments can prevent financial participation. Participating in financial environments may be challenging for people with stroke due to the communication disabilities they may experience. Understanding the experiences of adults with stroke-related communication disabilities in financial environments could help to identify strategies to improve their participation and performance. This study aimed to explore the experiences and potential challenges of people with post-stroke communication disabilities in financial environments, and introduce strategies for improving financial participation. Four adults, three being women, from [removed for blinding] with stroke-related speech and language disabilities participated in individual interviews for this qualitative descriptive research study. The data were analyzed using thematic analysis. Four themes and three subthemes were developed, highlighting that post-stroke communication disabilities often lead to financial exclusion due to external factors like institutional structures and social environments. Ableism, reflected in others' attitudes or behaviors, may have also impacted financial inclusion and participation. However, participants suggested strategies to improve financial participation and inclusion of people with stroke-related communication disabilities. This study adds to the growing area of speech-language pathology practice and research related to critical disability theory. Clinicians need to be aware that environments, including financial environments, have a key influence on the participation of people with post-stroke communication disabilities. Future studies should examine policies and practices in financial environments for people with stroke-related communication disabilities to improve their social inclusion.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| 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".