Comparing a Clinician Assisted and App-Supported Positive Psychiatry Behavioral Activation Intervention
Bibliographic record
Abstract
Abstract Positive psychiatry offers a unique approach to promote brain health and well-being in aging populations. Health interventions are increasingly becoming available using self-guided apps, however, little is known about the effectiveness of app technology or the difference between in-person versus self-guided app methodology for behavioural activation. The objective of this study was to investigate the difference in users and outcomes between two formats of a positive psychiatry intervention to promote brain health and well-being in later-life: (1) clinician-assisted, and (2) independent app use for self-management. As part of a larger national knowledge translation intervention two methods of a behavioural activation intervention (Clinician-assisted vs. Independent app use) were retrospectively compared. Main outcomes were patient characteristics (age, sex, and completion rate), psychological outcomes (health and resilience, and well-being), and behavioural outcomes (goal attainment, and items of goal SMART-ness). Clinician-assisted patients (n=254) were more likely to be male, older, and had lower health and resilience scores at baseline than Independent app users (n=333). Clinician-assisted patients had notably higher completion rates (99.2% vs. 10.8%). Psychological outcomes were similar regardless of intervention method for those who completed the intervention. Clinician-assisted patients had higher rates of goal attainment and goal SMART-ness. A preliminary goal setting methodology for effective behavioural activation, to promote brain health and wellness, is given. Clinician-patient relationships were found to be an important factor for intervention completion, caution is given for app use referral. Results indicate a need for further exploration to determine best practices for health app use in clinical practice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".