The Rising Implementation of Digital Health Platforms and Its Associated Inequities Among Patients with Mental Disorders: A Literature Review
Bibliographic record
Abstract
Introduction: Online health networks are a recent phenomenon and relevant research exploring the consequences of inequities marginalized groups may face in the context of digital health platforms is lacking. This paper aimed to address this gap by providing an introduction to the inequities that may be perpetuated through the increased use of digital health platforms with a focus on mental health disorders. Health inequities are important to study considering the rapid implementation of telemedicine and how they may potentially affect the usage and comprehension of digital platforms. Methods: PubMed was searched for articles examining health inequities in patients with mental health disorders. Our results yielded a total of 232 papers. Of these, twelve were included in the analysis. Results: Four major inequities were identified when assessing telemedicine in the mental health disorder populations: digital and medical literacy, age, socioeconomic factors and geographical location. Digital and medical literacy and age were reported to be perpetuated with the implementation of telemedicine. In contrast, socioeconomic difficulties and geographical location, rurality in particular, were in part resolved by the implementation of telemedicine. Discussion: The review shows that patients with mental health disorders experience the perpetuation or resolution of four major inequities through the use of telemedicine; digital/medical literacy, older age, socioeconomic factors and geographical location. Based on the literature, one may suggest modifications to user interfaces, increased digital support and income support, decreased cost to access and improved cellular coverage. As technological usage relating to access to medical services is ever-increasing, there is a pressing need for further investigation into the consequences and inequities that may be perpetuated or introduced. Conclusion: Future studies should look into other inequities and continue to explore potential solutions to reduce problems uncovered. Our research helps elucidate the potential paths that should be further explored and considered when implementing digital health platforms in order to efficiently and equitably help patients with mental health disorders.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".