FACT effectiveness in primary care; a single visit RCT for depressive symptoms
Bibliographic record
Abstract
Background Patients with depressive symptoms are common in primary care. Brief, simple therapies are needed. Aim Is a focussed acceptance and commitment therapy (FACT) intervention more effective than the control group for patients with depressive symptoms in primary care at one week follow up? Design and setting: A randomised, blinded controlled trial at a single primary care clinic in Auckland, New Zealand. Methods Patients presenting to their primary care practice for any reason were recruited from the clinic waiting room. Eligible patients who scored ≥2 on the PHQ-2 indicating potential depressive symptoms were randomised using a remote computer to intervention or control groups. Both groups received a psychosocial assessment using the “work-love-play” questionnaire. The intervention group received additional FACT-based behavioural activation activities. The primary outcome was the mean PHQ-8 score at one week. Results 57 participants entered the trial and 52 had complete outcome data after one week. Baseline PHQ-8 scores were similar for intervention (11.0) and control (11.7). After one week, the mean PHQ-8 score was significantly lower in the intervention group (7.4 vs 10.1 for control; p<0.039 one sided and 0.078 two sided). The number needed to treat to achieve a PHQ-8 score ≤6 was 4.0 on intention to treat analysis (p = 0.043 two sided). There were no significant differences observed on the secondary outcomes. Conclusion This is the first effectiveness study to examine FACT in any population. The results suggest that it is effective compared with control, at one week, for patients with depressive symptoms in primary care.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".