The promise of assistive technology in institutionalized old age care: economic efficiency, improved working conditions, and better quality of care?
Bibliographic record
Abstract
PURPOSE: Assistive technologies in care work are expected to alleviate the challenges related to population aging, namely the pressure on public budgets and a shortage of care professionals. This study examines how various stakeholders view the potentials of assistive technology in an institutionalized care setting in Denmark. METHOD: Using ethnographic field observations, interviews, and document analysis, we explore the residents', the staff's, and the municipality's perspectives on the technologies and analyze whether they live up to the stated expectations. RESULTS: We identify three parallel narratives representing each of the stakeholder's perspectives. The municipality's triple-win narrative emphasizes expected gains in terms of efficiency, improved working conditions, and better quality of care. The staff's ambiguity narrative contains both negative views regarding the motive for using technologies to save resources and positive accounts of how technologies have reduced work-related pain. The residents' limited agency narrative reflects an internalization of the staff's perspectives. CONCLUSIONS: We conclude that, despite both the staff and the municipality highlighting the residents' well-being and comfort as important outcomes of assistive technologies, the residents' wishes have limited influence on whether and, if so, how assistive technologies are used.IMPLICATIONS FOR REHABILITATIONDifferent stakeholders' views on assistive technology vary and are closely connected to their respective institutional interests and roles. These perspectives are important for a successful implementation of assistive technologies.Understanding the positions of staff and older care recipients, and differentiating between their respective perspectives can help both scholars and product developers understand the potentials and risks of AT in a more nuanced way.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".