Shifting Mindsets: The Impact of a Patient Portal on Functioning and Recovery in a Mental Health Setting
Bibliographic record
Abstract
OBJECTIVE: This study aims to understand whether higher use of a patient portal can have an impact on mental health functioning and recovery. METHOD: A mixed methods approach was used for this study. In 2019-2021, patients with mental health diagnoses at outpatient clinics in an academic centre were invited to complete World Health Organization Disability Assessment Scale 12 (WHODAS-12) and Mental Health Recovery Measure surveys at baseline, 3 months, and 6 months after signing up for the portal. At the 3-month time point, patients were invited to a semistructured interview with a member of the team to contextualize the findings obtained from the surveys. Analytics data was also collected from the platform to understand usage patterns on the portal. RESULTS: Overall, 113 participants were included in the analysis. There was no significant change in mental health functioning and recovery scores over the 6-month period. However, suboptimal usage was observed as 46% of participants did not complete any tasks within the portal. Thirty-five participants had low use of the portal (1-9 interactions) and 18 participants had high usage (10+ interactions). There were also no differences in mental health functioning and recovery scores between low and high users of the portal. Qualitative interviews highlighted many opportunities where the portal can support overall functioning and mental health recovery. CONCLUSIONS: Collectively, this study suggests that higher use of a portal had no impact, either positive or negative, on mental health outcomes. While it may offer convenience and improved patient satisfaction, adequate support is needed to fully enable these opportunities for patient care. As the type of interaction with the portal was not specifically addressed, future work should focus on looking at ways to support patient engagement and portal usage throughout their care journey.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".