Sustainability of Digital Home Care and Health Care Services in 2 Case Studies in Finland: Combined Climate and Social Impact Assessment
Bibliographic record
Abstract
Background: Digitalization is seen as a way to reduce the negative environmental impacts of health care production, but research is still limited. Objective: This study focuses on the assessment of the sustainability aspects of digital services in home care and health care. It demonstrates the approach to identify the climate impacts and social impacts-both positive and negative-on a selection of digital home care and health care services, such as medicine robot services for older home care clients, through 2 Finnish case studies. Methods: Impacts are identified from interviews and statistical data collected from public service providers and technology suppliers using both quantitative and qualitative assessments. Results: While a well-planned and well-implemented digital service is likely to be a climate-friendly option, every digitalization action carries at least some negative impacts. The design, architecture, and practical implementation of these services greatly affect their climate and social impacts. Conclusions: This study uses a novel combination of impact assessment methods, highlighting the importance of qualitative understanding alongside quantitative approaches for interpreting results, especially when numerical data are limited. Advocating for multimethod impact assessments is crucial to properly capturing the service context and promoting holistic sustainability thinking.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".