Identifying technology industry-led initiatives to address digital health equity
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.
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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 | 0.043 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".