Cross-cultural applicability of the episodic disability framework with adults living with HIV in Ireland: a qualitative study
Bibliographic record
Abstract
Purpose: To explore the cross-cultural applicability of the Episodic Disability Framework with people living with HIV in Ireland.Methods: We conducted a qualitative descriptive study using semi-structured interviews. We recruited adults with HIV from a hospital clinic and community-based organization in Dublin, Ireland. We asked participants to describe their health-related challenges, the extent to which these challenges were episodic in nature, and the impact on their overall health. We analyzed data using content analytical techniques.Results: Of the 12 participants (9 men; 3 women), with a median time since HIV diagnosis of 12 years, 83% had an undetectable viral load. Participants described multi-dimensional and episodic health challenges that spanned disability dimensions in the Episodic Disability Framework including: physical, cognitive, and mental-emotional symptoms and impairments, challenges to social inclusion, and uncertainty or worrying about future health with HIV. Contextual factors that exacerbated disability included stigma and fear of HIV disclosure whereas living strategies (lifestyle, positive outlook, resiliency) and social support (family, friends, health services and providers) mitigated disability.Conclusions: The Episodic Disability Framework is pertinent to adults living with HIV in Ireland. Contextual factors are critical for understanding cross-cultural distinctions of the disability experience across varying political, economic, socio-cultural, and environmental contexts and should be considered by rehabilitation professionals to address disability for people with HIV.Implications for rehabilitationThe Episodic Disability Framework, originally derived and validated from perspectives of adults living with HIV in Canada, is pertinent to people living with HIV in Ireland.The Episodic Disability Framework can be used to describe disability experienced by people living with HIV, and to better understand the contextual factors that may exacerbate or alleviate dimensions of disability over time.Contextual factors are critical for understanding cross-cultural distinctions of the disability experience across varying political, economic, sociocultural, and environmental contexts, and should be considered by rehabilitation professionals to better address disability for people with HIV.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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".