Impact of a mental health patient portal on patients’ views of compassion: a mixed-methods study
Bibliographic record
Abstract
Abstract Background Compassion is central to achieving positive clinical outcomes, commonly studied as a concept that enhances therapeutic alliance between patients and clinicians. Within mental health care, compassion may be enhanced by a patient portal, a digital platform where information is exchanged between clinicians and patients. The portal is viewed as a compassion-oriented technology, as it may positively influence safety, disease management, and patient engagement. As portals have limited implementation in mental health care, it is imperative to research the impact of portal use on patient’s perspectives of compassion expressed by clinicians. Methods We conducted a convergent mixed methods study to assess and understand the impact of portal use on patients’ experience of compassion in mental health care settings. The quantitative strand encompassed a self-administered survey consisting of a validated compassion scale at the time of enrolment in the portal and after both three and 6 months of portal use. The qualitative strand consisted of semi-structured interviews with patients after the three-month mark of portal use. Data collection and analysis of both strands happened independently, then these two complementary findings were merged narratively. Results A total of 113 patient surveys and ten interviews were included in analysis. The univariate model with time as the only independent variable did not show significant differences in the total compassion scores across the three time points, F (2, 135) = 0.36 p = 0.7. The model was then adjusted for sex, age, and diagnosis and did not show significant changes in the total compassion scores, F (2, 135) = 0.42 p = 0.66. Interview findings identified both positive and negative influences of portal use in patients’ perception of compassion. Some participants described compassion as something personal, not associated with the portal use. However, some participants reported that portals facilitated treatment experiences, being reflective of compassionate care. Conclusions Patient portals in mental health care may allow for timely exchange of information and create a space outside appointments to strengthen relationships between clinicians and patients, improving compassionate delivery of care. Further research can help better understand how portals can contribute to digital compassion as technological advancements continue to be integrated into mental health care contexts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".