Technology adoption and diffusion in healthcare at onset of COVID-19 and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents an overview of the effects of the COVID-19 pandemic on the adoption and diffusion of technologies including within healthcare. Consumer technologies have been rapidly applied to mitigate negative health impacts such as social isolation, or to monitor the health and function of family members separated by quarantine. As the lines between consumer technologies and professional health technologies blur, there is an opportunity to examine the outcomes of accessible and familiar technologies used by consumers. The rapid diffusion of technology uptake challenges traditional frameworks that describe technology acceptance and adoption. There is an opportunity to understand the impact of experience of use and involuntariness on technology diffusion. Beyond the onset of the pandemic, the management of post-COVID syndrome, which some see as the next public health crisis, is an opportunity to accelerate the diffusion of home monitoring technologies already benefiting people living with other chronic health conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it