Depression Outcomes in Adults Attending Family Practice Were Not Improved by Screening, Stepped-Care, or Online CBT during a 12-Week Study when Compared to Controls in a Randomized Trial
Bibliographic record
Abstract
There is uncertainty regarding possible benefits of screening for depression in family practice, as well as the most effective treatment approach when depression is identified. Here we examined whether screening patients for depression in primary care, and then treating them with different modalities, was better than treatment as usual alone. Screening was carried out for depression using the 9-item Patient Health Questionnaire (PHQ-9), with a score of ≥10 indicating significant depressive symptoms. PHQ-9 scores were given to family physicians prior to patients being seen (except for the Control group). Patients (n=1,489) were randomized to one of 4 groups. Group #1 were controls (n=432) in which PHQ-9 was administered but results were not shared. Group #2 was screening followed by treatment-as-usual (TAU) (n=426). Group #3 was screening followed by both TAU and the opportunity to use an on-line cognitive behavioural treatment (CBT) program (n=440). Group #4 utilized an evidence-based Stepped-care pathway for depression (n=191, note that this was not available at all clinics). Of the study sample 889 (60%) completed a second PHQ-9 rating at 12-weeks. There were no statistically significant differences in baseline PHQ-9 scores between these groups. Compared to baseline, mean PHQ-9 scores decreased significantly in the depressed patients over 12-weeks, but there were no statistically significant differences between any groups at 12-weeks. Thus, for those who were depressed at baseline Control group (Group #1) scores decreased from 15.3 ± 4.2 to 4.0 ± 2.6 (p<0.001), Screening group (Group #2) scores decreased from 15.5 ± 3.9 to 4.6 ± 3.0 (p<0.001), On-line CBT group (Group #3) scores decreased from 15.4 ± 3.8 to 3.4 ± 2.7 (p<0.01), and the Stepped-care pathway group (Group #4) scores decreased from 15.3 ± 3.6 to 5.4 ± 2.8 (p<0.05). In conclusion, these findings from this controlled randomized study do not suggest that using depression screening tools in family practice improves outcomes. They also suggest that much of the depression seen in primary care spontaneously resolves, and do not support suggestions that more complex treatment programs or pathways improve depression outcomes in primary care. Replications studies are required due to study limitations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".