Ageing with (and into) assistive technology: an exploration of the narratives of amputees and polio survivors
Bibliographic record
Abstract
PURPOSE: Assistive technologies (AT) perform an important social role, interacting with cultural systems to produce or hinder accessibility to biosocial environments. This interaction profoundly shapes not only how an individual body can be experienced by users but also produce and hinder accessibility to biosocial environments. AT users have historically been viewed through a medical model, which deems them disabled by their impairments and by dominant ableist narratives. Therefore, this paper serves to provide an insight into the importance of ageing with and into AT. This paper aims to investigate polio survivors' and diabetic amputees' experiences of assistive technologies in order to better understand impacts upon narrative and identity. By applying an anthropological and sociological lens, a holistic view of the experiences of polio survivor and amputee AT users is developed. METHOD: This paper draws on 16 in-depth interviews with polio survivors and diabetic amputees in the United States (US) and Australia, which were analysed using an experience-centered narrative approach. Both projects were approved by ethics boards. All participants provided written consent. RESULTS: Five themes were identified: a) disruption to biographies, which reflected AT impact on how narratives become altered; b) impacts to autonomy, which reflected the importance of regaining previous daily activities; c) re-engaging with community life, which highlighted how AT supported participation in valued activities; d) self-perceptions of assistive technologies, which act in opposition to external perspectives and challenge ableist narratives; and e) an intergenerational comparison of new and older AT users highlights the importance of temporalities. CONCLUSION: This paper offers new perspectives on ageing with assistive technologies, with a focus on identity and narrative. The importance of this paper is to contribute to the existing literature that demonstrates the cultural implications that arise through embodiment and assistive technologies.IMPLICATIONS FOR REHABILITATIONThe use of assistive technology can help individuals regain function, but the individual circumstances require considerationThe use of assistive technology is a complex entanglement of bodies, environments, biographies, and imagined futures.The use of assistive technology can provide participants autonomy over their narratives and assist with maintaining their identities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".