Identifying technology industry-led initiatives to address digital health equity
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
Objective: The COVID-19 pandemic has highlighted various barriers to health and the necessity of having access to digital health services. The technology industry can support addressing health barriers, promoting health equity and partnering with organizations to ensure access to digital health services for underserviced communities. The main objective of this study was to 1) identify what initiatives have been developed within the technology industry to address digital health equity; and to 2) determine whether these initiatives have been effective. Methods: A rapid review and a grey literature scan were conducted. The academic searches were performed using four databases, including Ovid MEDLINE, Scopus, CINAHL and PsychInfo. Two reviewers screened the articles for inclusion criteria. The grey literature scan was performed through Google and Million Short. Searches of technology industry initiatives were completed through scanning technology companies listed on the New York Stock Exchange, the Toronto Stock Exchange and iShares Expanded Tech Sector - Exchange Traded Fund. Results: Within the technology industry, 39 companies had relevant initiatives. These were identified as having one or more of the following: 1) having health-related collaborations with other companies, 2) promoting access to technology infrastructure and 3) delivering programs that supported notable inequities within the social determinants of health. Limited data are available on the effectiveness of these initiatives in reducing health inequities. Conclusions: As technology in the delivery of health services continues to evolve, health equity initiatives must be supported through innovative strategies. Partnering with the technology industry may be one way of addressing these health equity challenges.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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