Feeling excluded in financial environments: challenges and strategies to promote financial inclusion for adults with post-stroke communication disabilities
Bibliographic record
Abstract
Background: People with stroke-related communication disabilities often have challenges participating in social environments, which might include challenges in financial environments. Despite the importance of financial participation of people with stroke-related communication disabilities, this topic has not yet been studied. Understanding the experiences of this group of people in financial environments can improve their financial activities and participation. Objectives: This research study explores the experiences of people with stroke-related communication disabilities in financial environments with two main objectives: 1. to examine challenges experienced by adults living with stroke-related communication disabilities in financial environments; and 2. to identify strategies for improving financial inclusion for this group. Methods: I conducted a qualitative descriptive research study and recruited four adults (18+; three women, one man) who self-identified with stroke-related speech and language disabilities in Manitoba. I used semi-structured individual interviews with open-ended questions to explore their experiences in financial environments and analyzed the data using the thematic analysis approach. Results: The four themes and four subthemes that I developed highlight that stroke-related communication disabilities made financial participation more challenging and led to financial exclusion. Most of these challenges were associated with institutional structures and the social environment. Ableism, represented by others' attitudes and reactions, affected participants’ mental well-being and financial inclusion. However, participants also identified individual and environmental strategies to improve financial inclusion. One individual strategy is practicing and being prepared in a financial environment. An environmental strategy is raising staff’s awareness about stroke-related communication disabilities. Conclusion: The findings of this research study highlight that financial environments are not inclusive for people with stroke-related communication disabilities. Rehabilitation professionals and researchers need to be aware that environments have a key influence on financial inclusion and participation. Financial professionals and policymakers could improve policies and practices, ensuring that financial services and environments are accessible and inclusive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".