Clinical effectiveness of an ultra-brief intervention for common mental health syndromes in primary care: study protocol for a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Although mild to moderate mental health problems are common and often debilitating, treatment options in primary care settings in New Zealand are often severely limited for patients with these conditions. Previously, we developed an ultra-brief intervention (UBI) to address mild to moderate psychological concerns, designed to be delivered by primary care clinicians. Recent feasibility testing, including an adaptation for Māori individuals (the indigenous people of New Zealand), showed that the brief intervention was feasible and acceptable to both clinicians and their patients. This protocol describes a large pragmatic randomized controlled trial of our UBI in primary care settings across the greater Wellington region, compared with practice as usual. METHODS/DESIGN: We are using a two-arm cluster randomized controlled trial, with primary care practices randomized to exclusively deliver either the UBI or practice as usual to all their recruited participants. The structured, guided self-help UBI is delivered in three brief general practitioner (GP) appointments over a five week period. Participants are invited into the study based on partner primary health organization access criteria (youth, people with low income, or people with Māori or Pacific Island heritage). Improvements in mental health from baseline to post-treatment will be compared between the intervention and control groups using a mixed-models application of analysis of covariance. Data analysis will be on an intention-to-treat basis, to increase the real-world relevance of UBI and to meet the study's objective of releasing UBI to primary care clinicians nationwide. DISCUSSION: The UBI is a first-line intervention tool for GPs that models the stepped care approach advocated in New Zealand, against a background of limited access to treatments for often-overlooked patient groups. It is proposed to be accessible to clinicians and patients alike, with the potential to be relevant to primary care clinicians across New Zealand. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12613000041752.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.041 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.077 | 0.012 |
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".