Perspectives of Patients and Therapists on Social Media and Digital Data Use in Mental Health Therapy: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Incorporating insights from social media into the patient-provider encounter is increasingly being explored in health care settings. Less is known about the utility of these data in mental health therapy. OBJECTIVE: This study aims to prospectively investigate and characterize how social media and digital data are used in mental health therapy from both the patient and mental health therapist perspective. METHODS: Patients enrolled in mental health therapy and mental health therapists were interviewed using a semistructured interview guide. All interviews were transcribed and coded using a deductive framework analysis. Themes and subthemes were identified. Participants completed a sociodemographic survey, while mental health therapists also completed a behavioral norms and elicitation survey. RESULTS: Seventeen participants, that is, 8 (48%) mental health therapists and 9 (52%) patients were interviewed. Overall, participants identified 4 themes and 9 subthemes. Themes were current data collection practices, social media and digital data in therapy, advantages of social media and digital data in therapy, and disadvantages of social media and digital data in therapy. Most subthemes were related to the advantages and disadvantages of incorporating digital data in mental health therapy. Advantage subthemes included convenience, objective, builds rapport, and user-friendliness while disadvantage subthemes were nonreflective, ethically ambiguous, and nongeneralizable. The mental health therapists' behavioral norms and elicitation survey found that injunctive and descriptive normative beliefs mapped onto 2 advantage subthemes: convenience and objectivity. CONCLUSIONS: This qualitative pilot study established the advantages and disadvantages of social media and digital data use in mental health therapy. Patients and therapists highlighted similar concerns and uses. This study indicated that overall, both patients and therapists are interested in and are comfortable to use and discuss social media and digital data in mental health therapy.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".