Exploring mental health providers' interest in using web and mobile-based tools in their practices
Bibliographic record
Abstract
A growing number of Internet sites and mobile applications are being developed intended for use in clinical practice. However, during the development process (e.g., creating features and determining use cases), the needs and interests of providers are often overlooked. We explored providers' interests using a mixed-methods approach incorporating both qualitative and quantitative research methods. A first study used an interview approach to identify the challenges providers faced, tools they used, and any use of computers and apps specifically. Fifteen providers from both the United States and Canada completed the interview and recordings were transcribed and analyzed using a constructivist grounded theory approach. Four primary themes were identified including challenges, potential tools, access and usability. A second study used a brief survey completed by 132 providers at a large healthcare system to explore current use of and potential interest in Internet and mobile technologies. Although many providers (80.9%) reported recommending some form of technology to patients, this was mostly Internet websites that were predominantly informational/psychoeducational in nature. Overall, these studies combine to suggest a strong interest in websites and apps for use in clinical settings while highlighting potential areas (ease of use, patient security and privacy) that should be considered in the design and deployment of these tools.
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.
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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".