Client perceptions of the mental health engagement network: a qualitative analysis of an electronic personal health record
Bibliographic record
Abstract
BACKGROUND: Information technologies such as websites, mobile phone applications, and virtual reality programs have been shown to deliver innovative and effective treatments for mental illness. Much of the research studying electronic mental health interventions focuses on symptom reduction; however, to facilitate the implementation of electronic interventions in usual mental health care, it is also important to investigate the perceptions of clients who will be using the technologies. To this end, a qualitative analysis of focus group discussions regarding the Mental Health Engagement Network, a web-based personal health record and smartphone intervention, is presented here. METHODS: Individuals living in the community with a mood or psychotic disorder (n = 394) were provided with a smartphone and access to an electronic personal health record, the Lawson SMART Record, for 12 to 18 months to manage their mental health. This study employed a delayed-implementation design and obtained both quantitative and qualitative data through individual interviews and focus group sessions. Participants had the opportunity to participate in voluntary focus group sessions at three points throughout the study to discuss their perceptions of the technologies. Qualitative data from 95 focus group participants were analysed using a thematic analysis. RESULTS: Four overarching themes emerged from focus group discussions: 1) Versatile functionality of the Lawson SMART Record and smartphone facilitated use; 2) Aspects of the technologies as barriers to use; 3) Use of the Mental health Engagement Network technologies resulted in perceived positive outcomes; 4) Future enhancement of the Lawson SMART Record and intervention is recommended. DISCUSSION: These qualitative data provide a valuable contribution to the understanding of how smarttechnologies can be integrated into usual mental health care. Smartphones are extremely portable andcommonplace in society. Therefore, clients can use these devices to manage and track mental health issuesin any place at almost any time without feeling stigmatized. CONCLUSIONS: Assessing clients' perspectives regarding the use of smart technologies in mental health care provides an invaluable addition to the current literature. Qualitative findings support the feasibility of implementing a smartphone and electronic personal health record intervention with individuals who are living in the community and experiencing a mental illness, and provide considerations for future development and implementation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".