Engaging patients and family members in the evaluation of a mental health patient portal: protocol for a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Twenty per cent of Canadians will experience a mental illness in any year. Mental health patient portals have been developed to support these individuals in taking more control over their own mental health and care. This may be done through electronic access to their health records and other supportive functions like completion of online self-assessments. To date, there has been limited research into the value that these portals may provide within mental health contexts. This study will identify what value mental health patient portals may offer to patients and their family members. METHODS AND ANALYSIS: This study will use a mixed-methods design. Patients will complete a survey consisting of validated instruments at the time of enrolment in the portal, and at 3 and 6 months of portal use. Patient and family member focus groups will be conducted. Portal usage data will be collected to identify if there are differences in outcomes based on usage. The study will be done at Canada's largest mental health and addiction teaching hospital, and will be conducted using a patient and family-oriented research approach, engaging these important representatives in all stages of the research process. The primary data analysis for the survey portion of the study will be done using linear mixed-effect models, assessing the differences between patients with different portal usage levels. A thematic analysis will be conducted of the focus group transcripts. ETHICS AND DISSEMINATION: Approval from the study site's Research Ethics Board has been obtained. The dissemination of findings of this study will be done through presentations at conferences, as well as a formal peer-reviewed journal article. Additionally, the research team will work with a group of patients and family members to identify opportunities to complete knowledge translation and dissemination activities in non-traditional venues.
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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 | 0.099 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".