TECHNOSOCIAL INNOVATIONS IN THE HEALTHCARE SECTOR: WHAT ETHICAL ISSUES MIGHT WE FACE AS WE AGE?
Bibliographic record
Abstract
Abstract Technosocial innovations are designed to transcend current practices by linking technological development to community well-being. Their dazzling development is gradually transforming health services. Despite their recognized potential, a growing number of studies point to the ethical risks associated with their development. We are currently conducting a study to obtain a comprehensive picture of the ethical issues associated with the use of technosocial innovations for older adults and their care partners in the healthcare sector, and to identify key indicators of the presence of those ethical issues. A prospective design is use and combines a systematic literature review with a Delphi method. Of all the studies looking at the ethical issues involved in deploying technosocial innovations in the healthcare sector, a limited number have focused on those we may experience as we age. Still, six empirically-supported ethical issues have emerged : 1) exacerbation of the digital divide; 2) exclusion at multiple levels; 3) epistemic injustices; 4) infringement of dignity and confidentiality; 5) ageism; and 6) increased dependency dynamics. A number of indicators were identified in the early stages of the Delphi method, including a high rate of abandonment of the use of proposed innovations, and performance indicators with little integration of PREMs and PROMs. It is imperative to translate current knowledge into concrete resources that can be used in order to protect our rights and health as we age. This study is a first step towards identifying indicators that can be concretely used by health organizations wishing to act ethically.
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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 | 0.135 | 0.220 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".