Implementing a mental health app library in primary care: A feasibility study
Bibliographic record
Abstract
Confronted with a wide range of digital health tools (DHT), professionals and patients need guidance to use these tools correctly and optimize health management. In the fall of 2020, a DHT library developed by Quebec-based company TherAppX was implemented in 22 institutions. The library was designed to enable healthcare professionals to use DHT in clinical care. The purpose of the current study was to assess the feasibility of implementing the library, including user experience, changes in DHT recommendation habits, and factors that helped or hindered the implementation process. A multi-methods design focusing on secondary use of quantitative data collected by TherAppX and semi-structured interviews with users was employed. While the quantitative analyses indicated infrequent use of the library, qualitative analyses highlighted several factors that hindered its implementation, including certain library and user characteristics and the unprecedented context of the COVID-19 pandemic. Nevertheless, the quantitative analyses confirmed interest in DHT and their usefulness during follow-ups. The results revealed a marginally significant pre-post changes in the frequency with which DHT were recommended. This study helped identify areas for improvements and indicates that further evaluation is needed. Future implementations would benefit from ensuring optimal conditions for a successful implementation.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".