Conditions and ethical challenges that could influence the implementation of technologies in nursing homes: A qualitative study
Bibliographic record
Abstract
AIM: To explore the conditions that may influence the implementation of an interactive mobile application (app) and an intelligent videomonitoring system (IVS) in nursing homes (NHs) and the ethical challenges of their use. BACKGROUND: There is a lack of knowledge about implementing technologies in NHs and the ethical challenges that might arise. In past studies, nursing care teams expressed the need for technologies offering clinical support. Technologies like an IVS and an app could prove useful in NHs to prevent and manage falls and responsive behaviours. DESIGN: An exploratory qualitative study was conducted with care managers, family caregivers and formal caregivers in five NHs. METHODS: Each participant was shown a presentation of a potential app and a short video on an IVS. It was followed by an individual semi-structured interview. A conventional content analysis was performed. FINDINGS: Potential users found it would be possible to implement these technologies in NHs even if resistance could be expected. To facilitate adoption and achieve clinical benefits, the implementation of technologies should be pilot-tested, and coaching activities should be planned. Ethical risks were considered already present in NHs even without technologies, for example, risks to privacy. Strategies were proposed, for instance, to adapt the code of ethics and procedures. Some potential prejudices about the interest and abilities of older staff, nurses' aides, and family caregivers to use technology were identified. CONCLUSIONS: Through rigorous and ethical implementation, technologies supporting clinical care processes could benefit older people living in NHs, as well as their relatives and the staff. IMPLICATIONS FOR PRACTICE: Various strategies are proposed to successfully implement technologies. Effort should be made to avoid prejudices during implementation, and procedures should be adapted to mitigate possible ethical challenges.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".