Use of Antivibration Technology to Reduce Demands for In-Home Nursing Care and Support in Rural Settings for Persons with Essential Tremors: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: With the increased integration of technologies in the healthcare sector, it is important to understand the benefits emerging technologies may play to reduce demands on the health care system. The Steadiwear antivibration glove shows promise for enhancing the independence in functional abilities for persons with essential tremors and for alleviating the need for support from the health care system. The objective of this study was to examine Registered Nurses' (RN) perceptions of the potential for the Steadiwear antivibration glove to reduce the need for in-person support from community healthcare workers. METHODS: Eleven RNs, experienced in providing care in rural communities, participated in a semi-structured interview sharing their perspectives towards use of the Steadiwear antivibration glove in community practice settings. Thematic analysis guided by Braun and Clarke was undertaken. RESULTS: Nurses described the value of this technology to reduce client needs for support for activities of daily living (e.g., dressing, feeding) and independent activities of daily living (e.g., banking, transportation). CONCLUSIONS: Enhanced access to this technology may reduce the need for nursing and personal care support from the health system. Therefore the Steadiwear antivibration glove also shows potential to delay and/or prevent the need for more intensive support and mitigate the need for transition to a long-term care facility.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".