Physicians’ and Individuals’ Attitudes Toward Digital Mental Health Tools: Protocol for a Web Survey Study With Physician and Stakeholder Interviews
Bibliographic record
Abstract
Background Digital transformation is impacting health care delivery and showing great market dynamism, bringing opportunities and concerns alike. Digital health applications are a vibrant segment where regulation is emerging, with Germany paving the way with its DiGA program. Simultaneously, anxiety and depression constitute global health concerns, and their prevalence is expected to worsen due to the COVID-19 pandemic and its containment measures. Portugal and its National Health System may be a useful testbed for digital health interventions seeking to manage anxiety and depression. This research methodology is very relevant in studies on mental health, making the protocol highly reusable. Objective The paper outlines the protocol for a research project on the attitudes of physicians and potential users toward digital mental health apps to improve access to care and patient outcomes and to reduce the burden of disease for anxiety and depression. Methods Web surveys will be conducted to acquire data from main stakeholders (physicians and academic community). Data analysis will replicate studies from Dahlhausen and Borghouts to derive conclusions regarding the relative acceptance and likelihood of successful implementation of digital mental health apps in Portugal. Results The findings of the proposed studies will elicit important information on how physicians and individuals perceive digital mental health apps interventions to improve access to care and patient outcomes and to reduce the burden of disease for anxiety and depression. Conclusions The results of the studies projected in this research protocol will have implications for researchers and academia, industry, and policy makers regarding the adoption and implementation of digital health mental apps and associated interventions. Conflicts of Interest None declared.
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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 | 0.106 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".